Gas field construction project soil loss prediction method

Abstract

The invention discloses a gas field construction project soil loss prediction method, belongs to the field of environmental protection, and aims to improve prediction efficiency. Comprising the following steps: step 1, acquiring a soil loss influence factor; 2, screening the soil loss influence factors by adopting an analytic hierarchy process, constructing an importance judgment matrix, and screening out main influence factors; 3, predicting the soil loss prediction area according to three types of an excavation area, a backfill area and a recovery area; configuring a corresponding main influence factor for each type; 4, establishing a soil loss data database of the excavation area, the backfill area and the recovery area; and 5, carrying out regression analysis on the soil loss data database to obtain a soil erosion modulus calculation model. The constructed soil erosion model is simple, the number of parameters is small, the prediction accuracy is 85% or above, the calculation efficiency is improved by nearly 80%, and the purpose of greatly improving the calculation efficiency on the premise that the accuracy is guaranteed is achieved.

Description

Technical Field

[0001] The invention relates to the field of environmental protection, and in particular to a method for predicting soil loss in a gas field construction project. Background Art

[0002] In recent years, the state has issued a large number of policies and regulations on soil and water conservation, and has changed soil and water conservation compensation fees into taxes, indicating that the state attaches more and more importance to soil and water conservation, which has also brought greater pressure to enterprises in soil and water conservation. Until 2022, my country's natural gas production has reached 10 billion cubic meters for six consecutive years, gas field construction projects have blossomed in many places and have been launched rapidly, and the pressure of soil and water conservation work has risen sharply. Soil and water loss prediction is an important part of soil and water conservation work. Inaccurate prediction calculations will cause large deviations in the layout and investment of soil and water conservation measures, thereby bringing the risk of soil and water conservation changes, affecting the progress of the project and affecting the commissioning of gas fields. At present, the soil and water loss prediction of various industries in my country refers to the water conservancy industry standard "Guidelines for the Calculation of Soil Loss in Production and Construction Projects" (SL773-2018), but this guideline involves many parameters, the values ​​are subjective, and it is necessary to cooperate with the corresponding on-site measurement work, which makes the soil and water loss calculation time-consuming and difficult to obtain accurate values.

[0003] In order to further improve the efficiency of soil and water conservation in gas field construction projects, it is necessary to establish an efficient soil loss prediction method for gas field construction projects while ensuring prediction accuracy. Summary of the invention

[0004] The purpose of the present invention is to provide a method for predicting soil loss in a gas field construction project, so as to improve the prediction efficiency of soil loss prediction in a gas field construction project.

[0005] The technical solution adopted by the present invention is: a method for predicting soil loss in a gas field construction project, comprising the following steps:

[0006] Step 1: Obtain soil loss influencing factors;

[0007] Step 2: Use the analytic hierarchy process to screen the factors affecting soil loss, construct an importance judgment matrix, and screen out the main influencing factors;

[0008] Step 3: The soil loss prediction area is predicted according to three types: excavation area, backfill area and restoration area; and the corresponding main influencing factors are configured for each type;

[0009] Step 4: Establish a soil loss data database for excavation areas, backfill areas, and restoration areas;

[0010] Step 5: Perform regression analysis on the soil loss data database to obtain the calculation model of the soil erosion modulus.

[0011] Furthermore, the main influencing factors of the excavation area are rainfall erosivity factor R, soil erodibility factor K, slope length L and slope gradient S; the main influencing factors of the backfill area are rainfall erosivity factor R, soil erodibility factor K, slope length L and slope gradient S; the main influencing factors of the restoration area are rainfall erosivity factor R, soil erodibility factor K, slope length L, slope gradient S and vegetation coverage B.

[0012] Furthermore, in step 4, through soil and water conservation on-site monitoring and collected soil and water conservation monitoring and acceptance data, a soil loss data database of the excavation area, backfill area and restoration area is compiled.

[0013] Furthermore, the slope S, slope length L and vegetation coverage B of the excavation area, backfill area and restoration area are obtained through on-site measurement or drone aerial photography modeling.

[0014] Furthermore, the rainfall erosivity factor R and the soil erodibility factor K are obtained by querying the data.

[0015] Furthermore, the number of soil loss data of each type is greater than or equal to 100.

[0016] Furthermore, in step 1, soil loss influencing factors were obtained through literature review, expert consultation and case investigation.

[0017] Furthermore, the soil loss influencing factors in step 1 include 8 items: rainfall erosion factor R, soil erodibility factor K, elevation H, slope length L, slope S, temperature T, vegetation coverage B and wind speed W.

[0018] Furthermore, in step 2, eight soil loss influencing factors were screened using the analytic hierarchy process by consulting with experts in soil and water conservation, oil and gas.

[0019] Furthermore, the specific operation steps of step five are:

[0020] First, a regression analysis was performed on the soil loss data database to obtain the surface parameters of the excavation area, backfill area, and restoration area:

[0021] G 挖 =45.693+0.828S+1.191L;

[0022] G 填 =56.790+2.488S+0.846L;

[0023] G 恢复 =70.64+0.343S+0.653L-0.849B;

[0024] Next, calculate the soil erosion modulus:

[0025] M = R·K·G;

[0026] Where, M is soil erosion modulus, unit: t / hm 2 a;

[0027] R—rainfall erosivity factor;

[0028] K—soil erodibility factor;

[0029] G—Surface parameters, divided into G 挖 , G 填 , G 恢复 ;

[0030] S—slope, unit: degree;

[0031] L—slope length, unit: m;

[0032] B—vegetation coverage, %.

[0033] The beneficial effects of the present invention are as follows: the soil erosion model constructed by the present invention is simple, requiring only five parameters: slope S, slope length L, vegetation coverage B, soil erosion modulus M and rainfall erosion factor R. The number of parameters is small, and the calculation process of soil erosion modulus M is simple and convenient, which makes it easy to improve the prediction efficiency. The prediction accuracy is above 85%, and the calculation efficiency is improved by nearly 80%, achieving the purpose of greatly improving the calculation efficiency while ensuring the accuracy. The method is applied to the preparation of soil and water conservation plans, which can improve the efficiency and level of plan preparation and save more than 5% of labor costs. Moreover, the calculation is convenient and concise, and the calculation results are clear, which can effectively guide the continuous improvement of soil loss prediction technology for natural gas construction projects. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 The present invention discloses a calculation flow chart of the soil loss prediction method for a gas field construction project. DETAILED DESCRIPTION

[0035] The present invention is further described below in conjunction with the accompanying drawings and embodiments:

[0036] The method for predicting soil loss in a gas field construction project disclosed in the present invention comprises the following steps:

[0037] Step 1: Obtain soil loss influencing factors;

[0038] Step 2: Use the analytic hierarchy process to screen the factors affecting soil loss, construct an importance judgment matrix, and screen out the main influencing factors;

[0039] Step 3: The soil loss prediction area is predicted according to three types: excavation area, backfill area and restoration area; and the corresponding main influencing factors are configured for each type;

[0040] Step 4: Establish a soil loss data database for excavation areas, backfill areas, and restoration areas;

[0041] Step 5: Perform regression analysis on the soil loss data database and calculate the soil erosion modulus.

[0042] Among them, in step one, the soil loss influencing factors refer to the external factors that cause soil loss, including rainfall erosion factor, represented by R; soil erodibility factor, represented by K; elevation, represented by H; slope length, represented by L; slope, represented by S; temperature, represented by T; vegetation coverage, represented by B; wind speed, represented by W, a total of 8 items.

[0043] These eight factors affecting soil loss are all external factors that cause soil loss. However, some have greater impacts and some have smaller impacts. If all of them are taken into consideration, there will be too many parameters to be collected. Therefore, in step two, the factors affecting soil loss are screened and the main factors are selected.

[0044] Since each predicted plot has different regional types, such as some areas are excavation areas, some are backfill areas, and some are restoration areas, and the main influencing factors of soil loss in different areas are different. For example, the restoration area is covered with vegetation, while the vegetation in the excavation area and backfill area is destroyed, and the vegetation coverage rate is one of the main influencing factors of soil loss. In order to improve the accuracy of the prediction model, in step three, the soil loss prediction area is predicted according to the three types of excavation area, backfill area and restoration area; and the corresponding main influencing factors are configured for each type. The main influencing factors of the excavation area are rainfall erosivity factor R, soil erodibility factor K, slope length L and slope S; the main influencing factors of the backfill area are rainfall erosivity factor R, soil erodibility factor K, slope length L and slope S; the main influencing factors of the restoration area are rainfall erosivity factor R, soil erodibility factor K, slope length L, slope S and vegetation coverage B.

[0045] After the main influencing factors of each area type are determined, it is necessary to collect corresponding data to establish a prediction model. Therefore, in step 4, a soil loss data database for excavation areas, backfill areas, and restoration areas is established. The soil loss data database for the excavation area is composed of the collected data of rainfall erosion factor R, soil erodibility factor K, slope length L, and slope S. The soil loss data database for the backfill area is composed of the collected data of rainfall erosion factor R, soil erodibility factor K, slope length L, and slope S. The soil loss data database for the backfill area is composed of the collected data of rainfall erosion factor R, soil erodibility factor K, slope length L, slope S, and vegetation coverage B.

[0046] The data of rainfall erosivity factor R, soil erodibility factor K, slope length L, slope S and vegetation coverage B are obtained through soil and water conservation on-site monitoring and collected soil and water conservation monitoring and acceptance data. For example, the slope S, slope length L and vegetation coverage B data are obtained through on-site measurement or drone aerial photography modeling. The rainfall erosivity factor R and soil erodibility factor K are obtained by querying Appendix C of the "Guidelines for Calculating Soil Loss in Production and Construction Projects" (SL773-2018).

[0047] The present invention only needs to measure three parameters, namely, slope S, slope length L and vegetation coverage rate B, and only needs to query two parameters, namely, rainfall erosion factor R and soil erodibility factor K. The number of parameters is small and easy to obtain, which is conducive to improving prediction efficiency.

[0048] In order to improve the accuracy of the obtained model, the soil loss data of each type is greater than or equal to 100.

[0049] In order to quickly and accurately obtain the influencing factors of soil loss, in step one, the influencing factors of soil loss are obtained through literature review, expert consultation and case investigation.

[0050] In order to quickly screen out the main influencing factors, in step 2, eight soil loss influencing factors were screened using the analytic hierarchy process by consulting experts in soil and water conservation, oil and gas.

[0051] Step 5: The specific steps are:

[0052] First, a regression analysis was performed on the soil loss data database to obtain the surface parameters of the excavation area, backfill area, and restoration area:

[0053] G 挖 =45.693+0.828S+1.191L;

[0054] G 填 =56.790+2.488S+0.846L;

[0055] G恢复 =70.64+0.343S+0.653L-0.849B;

[0056] Next, calculate the soil erosion modulus:

[0057] M = R × K × G;

[0058] Where, M is soil erosion modulus, unit: t / hm 2 a;

[0059] R—rainfall erosivity factor;

[0060] K—soil erodibility factor;

[0061] G—Surface parameters, including G 挖 , G 填 , G 恢复 ;

[0062] S—slope, unit: degree;

[0063] L—slope length, unit: m;

[0064] B—vegetation coverage, %.

[0065] The soil erosion model constructed by the present invention is simple and only requires five parameters: slope S, slope length L, vegetation coverage B, soil erosion modulus M and rainfall erosion factor R. The number of parameters is small, the calculation process of the soil erosion modulus M is simple and convenient, the prediction efficiency is high, and the calculation result is clear. Applying this method in the preparation of soil and water conservation plans can improve the efficiency and level of plan preparation and save more than 5% of labor costs.

[0066] According to the present invention Figure 1 The soil loss prediction process of a drilling project in Sichuan is carried out according to the process. The specific calculation process is as follows:

[0067] 1. The project is located in the central part of Sichuan Province and a new drilling well will be built.

[0068] 2. Through on-site measurement and drone aerial photography modeling, the slope S, slope length L, and vegetation coverage B data of different areas of the project were obtained; by referring to Appendix C of the "Guidelines for Calculating Soil Loss in Production and Construction Projects" (SL773-2018), the rainfall erosion factor R and soil erodibility factor K data were obtained; the overall data are shown in the following table:

[0069] Statistical table of factors affecting soil loss in projects

[0070]

[0071] 3. According to the factors in the above table, they are respectively substituted into the soil erosion modulus calculation model for calculation.

[0072] as follows:

[0073] M = R·K·G;

[0074] G 挖 =45.693+0.828S+1.191L;

[0075] G 填 =56.790+2.488S+0.846L;

[0076] G 恢复 =70.64+0.343S+0.653L-0.849B;

[0077] The soil loss calculation results are as follows:

[0078] Soil loss calculation results table

[0079]

[0080] Comparative Example 1: The soil loss prediction for a project was carried out using the model recommended by the Guidelines for Calculating Soil Loss in Production and Construction Projects (SL773-2018).

[0081] Comparative Example 2: The soil loss of a project similar to that of Comparative Example 1 was actually measured using the actual measurement method.

[0082] Example 1: The method disclosed in the present invention is used to calculate soil loss for a project that is the same as that in Comparative Example 1.

[0083] The calculation results of Example 1 were compared with the calculation results of Comparative Example 1 and the measurement results of Comparative Example 2, mainly comparing the data accuracy and the result calculation time.

[0084] Based on the actual measured results, among which:

[0085] Guideline calculation accuracy = (1-|guideline calculation value-measured data| / measured value) × 100%;

[0086] Model calculation accuracy = (1-|model calculation value-actual measurement value| / actual measurement value) × 100%;

[0087] Improvement efficiency = (guideline calculation time - model calculation time) / guideline calculation time × 100%.

[0088] The results are shown in the table below:

[0089] Model Validation Calculation Table

[0090]

[0091]

[0092] As can be seen from the above table, the calculation accuracy of the present invention is 87.56%, which is only 6.41% lower than the calculation accuracy of the guideline (93.97%), but the calculation efficiency is improved by 77.20% compared with the guideline. In summary, the soil loss prediction method for gas field construction projects proposed by the present invention has an accuracy of more than 85%, and the calculation efficiency is improved by nearly 80%, achieving the purpose of greatly improving the calculation efficiency while ensuring the accuracy.

Claims

1. A method for predicting soil loss in a gas field construction project, characterized by: The following steps are involved: Step 1: Obtain soil loss influencing factors; Step 2: Use the analytic hierarchy process to screen the factors affecting soil loss, construct an importance judgment matrix, and screen out the main influencing factors; Step 3: Predict the soil loss prediction area according to three types: excavation area, backfill area and restoration area; And configure the corresponding main influencing factors for each type; Step 4: Establish soil loss database for excavation area, backfill area and restoration area; Step 5: Perform regression analysis on the soil loss database to obtain the calculation model of the soil erosion modulus.

2. The method for predicting soil loss in a gas field construction project according to claim 1, characterized in that: The main influencing factors of the excavation area are rainfall erosivity factor R, soil erodibility factor K, slope length L and slope S; The main influencing factors of the backfill area are rainfall erosivity factor R, soil erodibility factor K, slope length L and slope S; The main influencing factors of the restoration area are rainfall erosivity factor R, soil erodibility factor K, slope length L, slope gradient S and vegetation coverage B.

3. The method for predicting soil loss in a gas field construction project according to claim 2, characterized in that: In step 4, through soil and water conservation on-site monitoring and collected soil and water conservation monitoring and acceptance data, a soil loss database of the excavation area, backfill area and restoration area is compiled.

4. The method for predicting soil loss in a gas field construction project according to claim 3, characterized in that: Through on-site measurement or drone aerial photography modeling, the slope S, slope length L and vegetation coverage B of the excavation area, backfill area and restoration area are obtained.

5. The method for predicting soil loss in a gas field construction project according to claim 4, characterized in that: The rainfall erosivity factor R and soil erodibility factor K were obtained by querying the data.

6. The method for predicting soil loss in a gas field construction project according to any one of claims 1 to 5, characterized in that: The number of soil loss data of each type is greater than or equal to 100.

7. The method for predicting soil loss in a gas field construction project according to any one of claims 1 to 5, characterized in that: In step 1, soil loss influencing factors were obtained through literature review, expert consultation and case investigation.

8. The method for predicting soil loss in a gas field construction project according to any one of claims 1 to 5, characterized in that: The soil loss influencing factors in step one include rainfall erosion factor R, soil erodibility factor K, elevation H, slope length L, slope S, temperature T, vegetation coverage B and wind speed W.

9. The method for predicting soil loss in a gas field construction project according to claim 8, characterized in that: In step 2, eight soil loss influencing factors were screened using the analytic hierarchy process by consulting with experts in soil and water conservation, oil and gas.

10. The method for predicting soil loss in a gas field construction project according to any one of claims 2 to 5, characterized in that: Step 5: The specific steps are: First, a regression analysis was performed on the soil loss database to obtain the surface parameters of the excavation area, backfill area, and restoration area: G 挖 =45.693+0.828S+1.191L; G 填 =56.790+2.488S+0.846L; <h2 style=";text-align:left;direction:ltr">G<h2 style=";text-align:left;direction:ltr"> 恢复 <h2 style=";text-align:left;direction:ltr"> =70.64+0.343S+0.653L-0.849B; Next, calculate the soil erosion modulus: M = R × K × G; Where, M is soil erosion modulus, unit: t / hm 2 a; R—rainfall erosivity factor; K—soil erodibility factor; G—Surface parameters, divided into G 挖 , G 填 , G 恢复 ; S—slope, unit: degree; L—slope length, unit: m; B—vegetation coverage, %.

Images