A method for predicting interlayer interference coefficient of gas well

Abstract

The present application belongs to the technical field of oil and gas field development, and particularly relates to a method for predicting interlayer interference coefficient of gas well. The method firstly selects main control reservoir parameters affecting production contribution rate to determine single layer interference coefficient, and then selects sensitive main control reservoir parameter differential affecting interlayer interference coefficient to further determine interlayer interference coefficient. Through two-stage screening, the sensitive main control reservoir parameter differential best representing the interlayer interference coefficient is obtained for interference prediction, so that the interlayer interference degree can be accurately predicted. For the gas well with serious interference, the production layer can be timely adjusted or the production scheme can be optimized to promote efficient development. The prediction method can effectively and quantitatively predict the interlayer interference of commingled gas well. The method is not only suitable for predicting the interlayer interference of commingled well, but also suitable for predicting the new well to be put into production. Through specific gas field verification, the accuracy of the interlayer interference coefficient predicted by the method is high, and reliable prediction and quantitative evaluation of the interlayer interference can be realized.

Description

Technical Field

[0001] This invention belongs to the field of oil and gas field development technology, and specifically relates to a method for predicting the inter-layer interference coefficient of gas wells. Background Technology

[0002] In the development of multi-layer syngas wells, the problem of inter-layer interference has always existed. At present, the research on inter-layer interference in syngas wells is mainly based on indoor experiments, reservoir engineering, numerical simulation, field testing, and field statistics. The main research focuses on the formation conditions, seepage mechanism, and inter-layer interference correction coefficient of inter-layer interference in syngas wells.

[0003] Existing analyses of inter-layer interference mainly rely on qualitative, subjective determination of the intensity of inter-layer interference in syngas wells. This evaluation process is highly subjective and cannot achieve quantitative evaluation, making it difficult to quantitatively characterize inter-layer interference prediction.

[0004] For example, Chinese invention patent application CN112597644A discloses a method for full-cycle production capacity correction of multi-layer syngas reservoirs considering inter-layer interference. This method involves the calculation of interference coefficients and the correction of production capacity based on interference coefficients. However, the interference coefficients are mainly relative to the correction model and are only used for qualitative evaluation of the degree of interference. They are difficult to use for quantitative prediction of inter-layer interference. Summary of the Invention

[0005] The purpose of this invention is to provide a method for predicting the inter-layer interference coefficient of gas wells, which solves the problem that the evaluation process in the prior art is subject to strong subjective factors and that it is difficult to achieve quantitative characterization of inter-layer interference prediction.

[0006] To achieve the above objectives, the present invention provides a technical solution for predicting the inter-layer interference coefficient of gas wells, comprising the following steps:

[0007] 1) Utilize syngas wells with a history of gas production profile testing to obtain the production contribution rate of each production segment and the corresponding reservoir parameters of the syngas well.

[0008] 2) Determine the key reservoir parameters that affect the production contribution rate of syndicated gas wells from the reservoir parameters;

[0009] 3) Select a single-layer gas well, and based on the main control reservoir parameters, select the main control reservoir parameters, casing pressure, gas production and effective thickness of the production layer of the single-layer gas well, and establish a gas well single-layer gas production prediction model;

[0010] 4) Based on the gas production prediction model of a single layer of a gas well, calculate the theoretical single-layer gas production of each production layer of the syngas well and obtain the theoretical gas production of the syngas well. Then, based on the theoretical gas production and the actual gas production of the syngas well, determine the interlayer interference coefficient of the syngas well.

[0011] 5) Obtain the parameter differences of the main control reservoir in the syngas well and determine the parameter differences of the sensitive main control reservoir that affect the inter-layer interference coefficient; perform regression fitting on the inter-layer interference coefficient of the syngas well and the corresponding parameter differences of the sensitive main control reservoir to obtain the prediction model of the inter-layer interference coefficient of the gas well; or, directly perform regression fitting on the inter-layer interference coefficient of the syngas well and the parameter differences of the main control reservoir to obtain the prediction model of the inter-layer interference coefficient of the gas well.

[0012] 6) Obtain the main control reservoir parameters of the gas well to be predicted, obtain the corresponding main control reservoir parameter difference, and input it into the prediction model to predict the inter-layer interference coefficient of the gas well.

[0013] This invention first selects the main control reservoir parameters that have a significant impact on the production contribution rate of syndicated gas wells, establishes a gas well single-layer gas production prediction model and the corresponding inter-layer interference coefficient of syndicated gas wells, and further filters the sensitive main control reservoir parameter differences that have a significant impact on the inter-layer interference coefficient to obtain a gas well inter-layer interference coefficient prediction model. This improves the analysis efficiency, ensures that the parameters used in the analysis can fully characterize the gas well production situation, and avoids interference from too many parameters. Therefore, it can perform a more accurate quantitative analysis of the inter-layer interference coefficient of gas wells, reliably predict the degree of inter-layer interference in advance, and promptly identify gas wells with severe interference, thereby adjusting the production layer or optimizing the production plan in a timely manner.

[0014] Furthermore, the reservoir parameters mentioned in step 2) include: resistivity Rt, acoustic transit time AC, porosity φ, permeability K, and gas saturation Sg.

[0015] Furthermore, in step 2), by performing a cross-sectional analysis of sensitive factors on the production contribution rate and reservoir parameters of each production layer, the reservoir parameters whose correlation coefficient with the production contribution rate is greater than the first set threshold are identified as the main control reservoir parameters affecting the production contribution rate.

[0016] Furthermore, the first set threshold is 0.8.

[0017] Furthermore, the process of establishing the gas well single-layer gas production prediction model is as follows: taking the gas well casing pressure, effective thickness and main control reservoir parameters of the single-layer producing gas well as the independent variables and the gas production of the single-layer producing gas well as the dependent variable, the multivariate nonlinear regression fitting method is used to obtain the gas well single-layer gas production prediction model.

[0018] Furthermore, the gas production prediction model for a single layer of a gas well established by the multivariate nonlinear regression fitting method is Q. pred =[a*ln(I1*I2*I3*…*I n *P t )+b]*H; where I1, I2, I3…I nThe main control reservoir parameters are: Pt is the casing pressure of the gas well, H is the effective thickness, and a and b are constants obtained during fitting.

[0019] Furthermore, the inter-layer interference coefficient of the combined gas well is the ratio of the actual gas production of the combined gas well to the theoretical gas production of the combined gas well.

[0020] Further, in step 5), the method for obtaining the sensitive main control reservoir parameter level difference is as follows: for each syndicated gas well, obtain the mathematical level difference of different main control reservoir parameters, and through the sensitive factor cross-plot analysis of the level difference of each main control reservoir parameter index and the inter-layer interference coefficient, determine the main control reservoir parameter level difference that has a correlation coefficient greater than the second set threshold as the sensitive main control reservoir parameter level difference that affects the inter-layer interference coefficient.

[0021] Furthermore, the second set threshold is 0.8.

[0022] Furthermore, the process of establishing the gas well inter-layer interference coefficient prediction model is as follows: using the difference in sensitive main control reservoir parameters as the independent variable and the inter-layer interference coefficient as the dependent variable, a multivariate nonlinear regression fitting method is used to obtain the gas well inter-layer interference coefficient prediction model as η. pred =c*[d1*ln(I 1-mn )+d2*ln(I 2-mn )+d3*ln(I 3-mn )+…+d n *ln(I n-mn )]+e; where η is the inter-layer interference coefficient, I 1-mn I 2-mn I 3-mn …I n-mn For the sensitive main control reservoir parameter level difference, I n-mn In this context, the first independent 'n' represents the sensitive controlling reservoir parameter number, and 'mn' represents the parameter grade difference code, c, d1, d2, d3, d... n e and are constants obtained during fitting. Attached Figure Description

[0023] Figure 1 This is a flowchart of the gas well inter-layer interference coefficient prediction method of the present invention;

[0024] Figure 2a This is a graph showing the intersection of acoustic transit time and production contribution rate of each producing section of a syngas well in an embodiment of the gas well inter-layer interference coefficient prediction method of the present invention.

[0025] Figure 2b This is a cross-plot of resistivity and production contribution rate of each producing section of a syngas well in an embodiment of the gas well inter-layer interference coefficient prediction method of the present invention.

[0026] Figure 2cThis is a graph showing the intersection of permeability and production contribution rate of each producing section in a syngas well in an embodiment of the gas well inter-layer interference coefficient prediction method of the present invention.

[0027] Figure 2d This is a graph showing the intersection of porosity and production contribution rate of each producing section in a syngas well in an embodiment of the gas well inter-layer interference coefficient prediction method of the present invention.

[0028] Figure 2e This is a graph showing the intersection of gas saturation and production contribution rate of each producing section in a syngas well in an embodiment of the gas well inter-layer interference coefficient prediction method of the present invention.

[0029] Figure 3 This is a diagram showing the intersection of factors affecting the production of a single-well gas production and the gas production in an embodiment of the gas well inter-layer interference coefficient prediction method of the present invention.

[0030] Figure 4a This is a diagram showing the intersection of inter-layer interference coefficient and porosity difference in a combined gas well in an embodiment of the gas well inter-layer interference coefficient prediction method of the present invention.

[0031] Figure 4b This is a diagram showing the intersection of inter-layer interference coefficient and permeability difference in a combined gas well in an embodiment of the gas well inter-layer interference coefficient prediction method of the present invention;

[0032] Figure 4c This is a diagram showing the intersection of the inter-layer interference coefficient and the gas saturation level difference in a combined gas well, as illustrated in the embodiment of the gas well inter-layer interference coefficient prediction method of the present invention. Detailed Implementation

[0033] To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments.

[0034] Example of a method for predicting inter-layer interference coefficient in gas wells

[0035] The flowchart of the gas well inter-layer interference coefficient prediction method of the present invention is as follows: Figure 1 As shown, the following section uses a gas field A on the northern edge of the Ordos Basin as an example to provide a detailed explanation of the gas well inter-layer interference coefficient prediction method of the present invention.

[0036] Step 1: Select syngas wells with a history of gas production profile testing and obtain the production contribution rate of each production layer of the syngas well, as shown in Table 1. The production contribution rate of each production layer is the percentage obtained by dividing the gas production of each production layer by the sum of the gas production of all production layers.

[0037] Table 1

[0038]

[0039]

[0040] Step 2, as follows Figures 2a-2e As shown, resistivity Rt, acoustic transit time AC, and porosity are used. Reservoir parameters such as permeability K and gas saturation Sg were subjected to sensitivity analysis using cross-plots with the production contribution rate of each producing section of the syngas well. Reservoir parameters with a correlation coefficient R ≥ 0.8 were identified as the main controlling reservoir parameters affecting production contribution rates. In this embodiment, reservoir porosity was determined. The three parameters of permeability K, gas saturation Sg are the main reservoir parameters that affect the production contribution rate of each producing section of the syngas well.

[0041] in, Figure 2a This is a cross-plot of acoustic transit time and production contribution rate for each producing section of a syngas well. Figure 2b This is a cross-plot showing the resistivity and production contribution rate of each producing section in a combined gas well. Figure 2c This is a cross-plot showing the permeability and production contribution rate of each producing section of a syngas well. Figure 2d This is a cross-plot showing the interpolation of porosity and production contribution rate in each producing section of a syngas well. Figure 2e This is a cross-plot showing the gas saturation and production contribution rate of each producing section in a combined gas well.

[0042] Step 3, as follows Figure 3 As shown, the main reservoir parameters (porosity) of a single-production gas well are used. Permeability (K), gas saturation (Sg), effective thickness (H), and casing pressure (P) of the gas well. t A multivariate nonlinear fitting was performed with the gas production of a single-layer gas well to form a single-layer gas production prediction model:

[0043]

[0044] Its correlation coefficient R = 0.8599.

[0045] Step 4, as follows Figures 4a-4c As shown, a single-layer gas production prediction model for gas wells is used to calculate the gas production of each single layer in a combined-production gas well. The calculated gas production of each single layer in the combined-production gas well is then summed to obtain the theoretical gas production of the combined-production gas well. Finally, based on the theoretical gas production and the actual gas production of the combined-production gas well, the inter-layer interference coefficient of the combined-production gas well is determined; where the inter-layer interference coefficient is the ratio of the actual gas production to the theoretical gas production of the combined-production gas well. Figure 4a This is a cross-plot of the inter-layer interference coefficient and porosity difference in syngas wells. Figure 4b This is a cross-plot of the inter-layer interference coefficient and permeability difference in syngas wells. Figure 4c This is a cross-plot of the inter-layer interference coefficient and gas saturation level difference in syngas wells. In this embodiment, the inter-layer interference coefficient of the syngas wells is shown in Table 2.

[0046] Table 2

[0047]

[0048]

[0049] Step 5: Obtain the main control reservoir parameter difference using the mathematical difference calculation method. Example: Main control reservoir parameter difference (porosity difference) for syngas wells. Permeability difference K mn Gas saturation grade difference Sg mn As shown in Table 3, the main control reservoir parameter differentials (porosity differentials) are adopted. Permeability difference K mn Gas saturation grade difference Sg mn Cross-plot analysis of sensitive factors was performed on the inter-layer interference coefficients of the combined gas wells. The main control reservoir parameter differences with a correlation coefficient R ≥ 0.80 were identified as the sensitive main control reservoir parameter differences affecting the inter-layer interference coefficient, as shown in Figure 4. In this embodiment, the reservoir porosity difference... Permeability difference K mn Gas saturation grade difference Sg mn The three parameter differences are the sensitive master reservoir parameter differences of the inter-layer interference coefficient.

[0050] Table 3

[0051]

[0052]

[0053] Step 6, adjust the porosity levels. Permeability difference K mn Gas saturation grade difference Sg mn The four parameters of the inter-layer interference coefficient η in combined and uncombined gas wells were fitted using a multivariate nonlinear regression method to establish a prediction model for the inter-layer interference coefficient of combined and uncombined gas wells. The expression is as follows: This allows us to predict the inter-layer interference coefficient η of syngas wells. pred .

[0054] As shown in Table 4, in this embodiment, the predicted inter-layer interference coefficient obtained using the inter-layer interference prediction model for syndicated gas wells is compared with the actual inter-layer interference coefficient. For the syndicated gas wells in this embodiment, if the error between the actual and predicted inter-layer interference coefficients of multi-layer syndicated gas wells is within 20%, it is considered to be in compliance. Therefore, it can be seen that, except for wells #5 and #19, the prediction results of the remaining wells are in compliance, with a compliance rate of 92.3%.

[0055] Table 4

[0056]

[0057]

[0058] After a new well in a joint-production well is put into production, the actual inter-layer interference coefficient of that well can be obtained. The obtained actual inter-layer interference coefficient is compared with the predicted inter-layer interference coefficient. If the prediction error exceeds an acceptable range, the relevant sensitive main control reservoir parameters and the actual inter-layer interference coefficient of that joint-production well are added to the sample database, and step 6 is repeated to obtain the corrected prediction model for the inter-layer interference coefficient of the joint-production gas well. With more predictions, verifications, and corrections for new wells, the prediction accuracy of the inter-layer interference coefficient prediction model for joint-production gas wells will continuously improve.

[0059] The prediction method of this invention can effectively and quantitatively predict inter-layer interference in syngas wells. It is applicable not only to the prediction of inter-layer interference in syngas wells that have already been put into production, but also to the prediction of new wells that are about to be put into production. Through two-stage screening, the difference in the sensitive main control reservoir parameters that have a greater impact on the inter-layer interference coefficient is finally obtained for interference prediction. Therefore, it can accurately predict the degree of inter-layer interference in production. For syngas wells with severe inter-layer interference, the production layer can be adjusted in time or the production plan can be optimized to promote the efficient development of the gas field.

[0060] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for predicting the inter-layer interference coefficient of a gas well, characterized in that, Includes the following steps: 1) Utilize syngas wells with a history of gas production profile testing to obtain the production contribution rate of each production segment and the corresponding reservoir parameters of the syngas well. 2) Determine the key reservoir parameters that affect the production contribution rate of syndicated gas wells from the reservoir parameters; 3) Select a single-layer gas well, and based on the main control reservoir parameters, select the main control reservoir parameters, casing pressure, gas production and effective thickness of the production layer of the single-layer gas well, and establish a gas well single-layer gas production prediction model; 4) Based on the gas well single-layer gas production prediction model, calculate the theoretical single-layer gas production of each producing section of the syngas well, and obtain the theoretical gas production of the syngas well. Then, based on the theoretical gas production and the actual gas production of the syngas well, determine the inter-layer interference coefficient of the syngas well. The process of establishing the gas well single-layer gas production prediction model is as follows: with the casing pressure, effective thickness and main control reservoir parameters of the single-layer production gas well as the independent variables and the gas production of the single-layer production gas well as the dependent variable, the multivariate nonlinear regression fitting method is used to obtain the gas well single-layer gas production prediction model. 5) Obtain the grade difference of the main control reservoir parameters of the syngas well and determine the grade difference of the sensitive main control reservoir parameters that affect the inter-layer interference coefficient; perform regression fitting on the inter-layer interference coefficient of the syngas well and the corresponding grade difference of the sensitive main control reservoir parameters to obtain the prediction model of the gas well inter-layer interference coefficient; the process of establishing the prediction model of the gas well inter-layer interference coefficient is as follows: using the grade difference of the sensitive main control reservoir parameters as the independent variable and the inter-layer interference coefficient as the dependent variable, the multivariate nonlinear regression fitting method is used to obtain the prediction model of the gas well inter-layer interference coefficient as η. pred = c*[d1*ln(I1-mn)+d2*ln(I 2-mn )+d3*ln(I 3-mn )+…+d n *ln(I n-mn )]+e; where η pred I is the inter-layer interference coefficient. 1-mn I 2-mn I 3-mn …In-mn represents the parameter difference between the sensitive controlling reservoirs, I n-mn In this context, the first independent 'n' represents the sensitive controlling reservoir parameter number, and 'mn' represents the parameter grade difference code, c, d1, d2, d3, d... n And e are constants obtained during fitting; 6) Obtain the main control reservoir parameters of the gas well to be predicted, obtain the corresponding main control reservoir parameter difference, and input it into the prediction model to predict the inter-layer interference coefficient of the gas well.

2. The method for predicting the inter-layer interference coefficient of gas wells according to claim 1, characterized in that, The reservoir parameters mentioned in step 2) include: resistivity Rt, acoustic transit time AC, porosity φ, permeability K, and gas saturation Sg.

3. The method for predicting the inter-layer interference coefficient of gas wells according to claim 1, characterized in that, In step 2), by performing a cross-sectional analysis of sensitive factors on the production contribution rate and reservoir parameters of each production layer, the reservoir parameters whose correlation coefficient with the production contribution rate is greater than the first set threshold are identified as the main control reservoir parameters affecting the production contribution rate.

4. The method for predicting the inter-layer interference coefficient of gas wells according to claim 3, characterized in that, The first set threshold is 0.

8.

5. The method for predicting the inter-layer interference coefficient of gas wells according to claim 1, characterized in that, The gas well single-layer gas production prediction model established by the multivariate nonlinear regression fitting method is Q. pred =[a*l n (I1*I2*I3*…*I n *P t )+b]*H; where I1, I2, I3…I n As the main controlling reservoir parameter, P t Let H be the casing pressure of the gas well, H be the effective thickness, and a and b be constants obtained during fitting.

6. The method for predicting the inter-layer interference coefficient of gas wells according to claim 1, characterized in that, The inter-layer interference coefficient of the combined gas well is the ratio of the actual gas production of the combined gas well to the theoretical gas production of the combined gas well.

7. The method for predicting the inter-layer interference coefficient of gas wells according to claim 1, characterized in that, In step 5), the method for obtaining the sensitive main control reservoir parameter level difference is as follows: For each syndicated gas well, obtain the mathematical level difference of different main control reservoir parameters. By performing a sensitive factor cross-plot analysis on the level difference of each main control reservoir parameter index and the inter-layer interference coefficient, determine the main control reservoir parameter level difference whose correlation coefficient with the inter-layer interference coefficient is greater than the second set threshold.

8. The method for predicting the inter-layer interference coefficient of gas wells according to claim 7, characterized in that, The second set threshold is 0.8.

Images