A method for predicting liquid loading in horizontal wells based on gas well production data

Abstract

The present invention proposes a method for predicting liquid accumulation in horizontal wells based on gas well production data, comprising the following steps: Step S1: Acquire static data and instantaneous data of the target gas well; Step S2: Preprocess the data; Step S3: Fit the production history of the gas well and analyze the current production status of the gas well; Step S4: Predict the production performance of the gas well in the future and screen the optimal production system of the target gas well. The present invention overcomes the problem that the existing model needs to measure key data for gas reservoir engineering evaluation such as daily gas production and daily water production, and can simulate the liquid accumulation situation in the gas well by only inputting oil pressure, casing pressure, and switch well status. Compared with the traditional gas well liquid accumulation prediction model, the present invention can perform simulation and prediction for different gas field blocks, and can screen out the optimal production system to guide gas well production.

Description

Technical Field

[0001] The invention relates to the technical field of natural gas exploitation, and in particular to a method for predicting liquid accumulation in a horizontal well based on gas well production data. Background Art

[0002] Most gas reservoirs are partially or completely surrounded by formation water in the same layer outside the boundary. During the development of gas reservoirs, due to the decrease in gas layer pressure, the formation water in the periphery invades the gas reservoir, sometimes breaks through the bottom of the well, causing the gas well to produce water. The two-phase flow of gas and water increases the seepage resistance, causing the gas well production to drop sharply, or even flooding, which significantly reduces the recovery rate of the gas reservoir. Due to the invasion of edge and bottom water, part of the natural gas is isolated by formation water, making the recovery rate of water-driven gas reservoirs much lower than that of gas-driven gas reservoirs. Since the impact of bottom well liquid accumulation on production capacity is not easy to detect in the early stage, and the damage will take a long time to be noticed, it is often difficult to judge the wellbore liquid accumulation. Therefore, it is necessary to carry out research on horizontal well liquid accumulation prediction methods to optimize the production system.

[0003] At present, the prediction models for gas well liquid accumulation are mainly ellipsoid model (Li Min, Guo Ping, Tan Guangtian. New view on gas well liquid carrying [J]. Petroleum Exploration and Development, 2001, 28 (5): 105-106.) and spherical cap model (Wang Yizhong, Liu Qingwen. A new method for calculating the minimum critical flow rate of gas well liquid carrying [J]. Daqing Petroleum Geology and Development, 2007, 26 (6): 82-85.). These two models compare the calculated critical flow rate (flow rate) with the actual gas production to determine whether the gas well is in the liquid accumulation state. However, the above two models cannot identify gas wells in the liquid accumulation stage; they cannot detect the bottom well liquid accumulation early and guide the gas field to carry out drainage and gas production operations at the beginning of liquid accumulation. In the invention patent (patent publication number CN113338915A), a method for judging whether a gas well is filled with liquid and predicting the height of the liquid accumulation is disclosed. The annular static gas column pressure gradient is calculated by obtaining the gas well production parameters and the annular static gas column pressure curve is drawn. The tubing flow pressure gradient is calculated from the tubing shoe upwards and the tubing flow pressure curve is drawn using the newly proposed liquid holdup model, thereby obtaining the calculated wellhead oil pressure, which is compared with the actual oil pressure to judge whether the gas well is filled with liquid. If the gas well is filled with liquid, the tubing gas core pressure gradient is calculated from the wellhead downwards using the actual oil pressure and the tubing gas core pressure curve is drawn. The depth corresponding to the intersection of the curve and the wellbore flow pressure curve is used to determine the wellbore liquid accumulation position. This method can predict the height of the liquid accumulation, but it cannot optimize the production system. At the same time, some existing calculation models require intensive bottom hole flow pressure test data, measured wellhead liquid production data, etc., which limits the further promotion of the calculation model in the oil field. Summary of the invention

[0004] The present invention mainly overcomes the deficiencies in the prior art and aims to provide a method for predicting liquid accumulation in horizontal wells based on gas well production data.

[0005] In order to achieve the above technical objectives, the present invention adopts the following technical solutions:

[0006] 1. A method for predicting liquid loading in a horizontal well based on gas well production data, characterized in that it comprises the following steps:

[0007] S1, obtaining static data and instantaneous data of the target gas well;

[0008] S2, preprocess the data;

[0009] S3, fitting the production history of the gas well and analyzing the current production status of the gas well;

[0010] S4, predict the production performance of the gas well in the future and select the optimal production system for the target gas well.

[0011] Furthermore, the static data includes wellbore structure, fracturing data, and formation properties; the instantaneous data includes date, valve status, oil pressure, casing pressure, and instantaneous gas volume.

[0012] Furthermore, the detailed steps of step S3 are:

[0013] S31, calculating the wellbore fluid loading state at the initial moment;

[0014] S32, construct a priori fitting process pool with high individual number and low evolutionary generation, simulate and evaluate production history of different parameter groups, and screen feasible solution groups;

[0015] S33, construct a posterior fitting process pool with a moderate number of individuals and a moderate number of evolutionary generations, combine the prior fitting results, simulate and evaluate the production history of different parameter groups, and select the optimal solution.

[0016] Furthermore, the detailed steps of step S4 are:

[0017] S41, generate different spacing schemes and predict the production performance of different schemes;

[0018] S42, counting the amount of liquid accumulation in the wellbore, the relationship between the change of oil and casing pressure, the gas production and the water production corresponding to different spacing schemes;

[0019] S43, screening the optimal production system.

[0020] Beneficial effects:

[0021] Compared with the prior art, the present invention has the following beneficial effects:

[0022] The present invention overcomes the problem that existing models need to measure key data for gas reservoir engineering evaluation such as daily gas production and daily water production. Only oil pressure, casing pressure, and switch well status are required to simulate gas well liquid accumulation. Compared with traditional gas well liquid accumulation prediction models, the present invention can simulate and predict different gas field blocks, and can also select the optimal production system to guide gas well production. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 It is a flow chart of a method for predicting liquid accumulation in a horizontal well based on gas well production data according to an embodiment of the present invention;

[0024] Figure 2 This is a diagram of the measured instantaneous casing pressure of the X2 well in an embodiment of the present invention;

[0025] Figure 3 This is a graph of the measured instantaneous gas production of the X2 well in an embodiment of the present invention;

[0026] Figure 4 The X2 well casing pressure fitting curve of the embodiment of the present invention;

[0027] Figure 5 The X2 well production fitting curve of the embodiment of the present invention;

[0028] Figure 6 A comparison diagram of vertical depth of liquid accumulation in the oil pipe of the X2 well according to an embodiment of the present invention;

[0029] Figure 7 This is a comparison diagram of the vertical depth of the annular fluid accumulation in the X2 well according to an embodiment of the present invention. DETAILED DESCRIPTION

[0030] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0031] Liquid accumulation and production optimization have always been key challenges in the natural gas extraction process. This application uses modern data acquisition and analysis technology to combine static data (such as wellbore structure, formation properties) and instantaneous data (such as real-time production data) for comprehensive analysis and prediction. First, by collecting static and instantaneous data of gas wells, the technology obtains a deep understanding of the characteristics and operating status of gas wells. These data play a key role in the prediction and optimization process. Then the preprocessing stage of the technology is done to ensure the accuracy and consistency of the data. Cleaning and formatting the data helps to eliminate noise and make it more analyzable. Then by fitting and simulating the production history of the gas well, the technology is able to evaluate the current production status, predict liquid accumulation, and understand formation pressure and other key parameters. This helps to identify and solve liquid accumulation problems in advance. Most importantly, this application is not limited to solving current problems, but also provides a way to optimize future production of gas wells through simulation and statistical techniques. By generating different combinations of production parameters, the best production system is screened to improve the production efficiency of gas wells and reduce costs.

[0032] Specifically, Figure 1 As shown, the present invention proposes a method for predicting liquid accumulation in a horizontal well based on gas well production data, comprising the following steps:

[0033] Step S1, acquiring static data and instantaneous data of a target gas well;

[0034] Specifically, the purpose of this step is to collect static and instantaneous data of the gas well so as to fit the production history of the gas well in the subsequent steps. These data are used to establish a complete description of the characteristics and operating status of the gas well. These data are the basis for analysis and prediction. Specific data items include: static data and instantaneous data, the static data includes wellbore structure, fracturing data, and formation properties, where the wellbore structure parameters include tubing inner diameter, tubing outer diameter, tubing length, casing inner diameter, casing outer diameter, casing length, whether there is a downhole packer, packer position, pipe wall roughness, depth measurement, vertical depth, well inclination, azimuth, completion method; fracturing data includes perforation top depth, perforation bottom depth; formation properties include formation temperature, original formation pressure, porosity, permeability, gas saturation, fluid PVT (pressure, volume, temperature) data. And instantaneous data includes date, valve status, oil pressure, casing pressure, instantaneous gas volume, where the date records the timestamp of the data to establish a time series of data. The valve status describes the open or closed state of the valve in the well, which affects the production control of the gas well. The oil pressure and casing pressure parameters indicate the pressure of the tubing and casing, which are important for understanding the pressure distribution of the fluid in the well. The instantaneous gas volume records the instantaneous production data at the wellhead, which is used to understand the production situation of the well.

[0035] Step S2: pre-process the data and check for abnormal data. Specifically, see the following table:

[0036] Table 1 Data preprocessing

[0037]

[0038] Step S3, fitting the gas well production history and analyzing the current production status of the gas well;

[0039] According to the processed data, the production history of the gas well is fitted, the current production status of the gas well is analyzed, and the current production situation of the gas well is evaluated to provide a basis for the optimization of the production system. This step aims to find the best combination of production parameters through simulation, evaluation and screening to best fit the actual production history, evaluate the current production status, and provide a basis for the optimization of the production system. The specific steps are as follows:

[0040] S31, calculating the wellbore fluid loading state at the initial moment;

[0041] Specifically, this step is to determine the initial state of the analysis time period, usually using existing static data and instantaneous data. The initial wellbore fluid loading state is the starting point of the analysis, which is crucial to the accuracy of subsequent simulation and analysis.

[0042] S32, construct a priori fitting process pool with high individual number and low evolutionary generation, simulate and evaluate production history of different parameter groups, and screen feasible solution groups;

[0043] Specifically, this step is to create a priori fitting process pool containing multiple parameter groups, each of which represents a different possible production parameter setting. Through simulation and production history evaluation, this process aims to screen out possible production plans, that is, which parameter combinations can better fit the actual production history data.

[0044] S33, construct a posterior fitting process pool with a moderate number of individuals and a moderate number of evolutionary generations, combine the prior fitting results, simulate and evaluate the production history of different parameter groups, and select the optimal solution.

[0045] Specifically, based on the results of the prior fitting, a posterior fitting process pool containing fewer parameter groups is created. This process pool combines the experience of the prior fitting to further narrow the parameter range and optimize the parameter combination to find the production solution that best suits the actual production history.

[0046] Step S4, predicting the production performance of the gas well in the future and selecting the optimal production system for the target gas well.

[0047] Specifically, for horizontal gas wells that have completed production history matching, detailed data analysis and system optimization are carried out to predict the production performance of the gas wells in the future, and the optimal production system of the target gas wells is compared and selected, including the following steps:

[0048] S41, generate different spacing plans and predict the production performance of different plans;

[0049] Specifically, this step is to generate multiple different production parameter settings or interval scenarios, which include different wellbore pressures, production rates, valve control strategies, etc. The purpose of generating multiple scenarios is to consider different production strategies to understand their impact on future production performance. Simulating these scenarios can help evaluate the advantages and disadvantages of each scenario to determine the best production system.

[0050] S42, counting the amount of liquid accumulation in the wellbore, the relationship between the change of oil and casing pressure, the gas production and the water production corresponding to different spacing schemes;

[0051] Specifically, in this step, the key parameter changes corresponding to different spacing schemes are recorded and analyzed through simulation and statistics. This includes wellbore fluid loading, oil and casing pressure, gas production, and water production. By statistically analyzing and comparing the changes in these parameters, we can understand the impact of different production strategies, including their impact on wellbore fluid loading management, oil and casing pressure control, and gas and water production. This helps determine which strategy may be more advantageous in actual operations.

[0052] S43, screening the optimal production system.

[0053] Specifically, in this step, the most suitable production system for actual production is selected based on the simulation and statistical results. The optimal system is the strategy that shows the best production performance in the simulation results and meets the production goals and economic requirements. The goal of this step is to develop the most reasonable production strategy to improve the production efficiency of gas wells, reduce operating costs, and ensure good production performance in the future.

[0054] Example 1.

[0055] The instantaneous production data of Well X2 from August 30, 2021 to November 3, 2021 were selected as the evaluation object, and the static data of Well X2 were collected. The instantaneous oil-casing pressure difference was measured. Figure 2 , the measured instantaneous gas production is shown in Figure 3 The production of this well has the following characteristics: (1) Production system: This well is a typical frequent intermittent production well, with occasional long-closed and long-open systems; (2) Changes in oil-casing pressure: The wellbore has obvious liquid accumulation characteristics, and the average oil-casing pressure difference is above 4.5 MPa; (3) Changes in gas production: The data fluctuations basically conform to the production characteristics of the frequent intermittent system. The gas production at the moment of sudden well opening can reach 2 to 6.8×10 4 m 3 / d, after a short period of well production, the gas production dropped to 0.2-0.6×10 4 m 3 / d; (4) Liquid drainage method: The liquid drainage method of this well is mainly to open the well suddenly after shutting in each intermittent cycle to make the gas production greater than the critical liquid carrying flow rate, so as to quickly discharge the accumulated liquid in the wellbore.

[0056] Preprocess the data and check for abnormal data. Fit the gas well production history based on the processed data. The analysis results of the fitting parameters of the production history of well X2 are shown in Table 2. The fitting curves of oil pressure and casing pressure are shown in Table 2. Figure 4 , gas production fitting curve see Figure 5 After completing the production history matching, the formation energy, wellbore liquid loading status and other results will be calculated, and the liquid loading depth of the tubing and casing annulus will be compared with the actual results of the acoustic liquid level test. Figure 6 , Figure 7 .

[0057] Table 2 Analysis results of production history matching parameters of Well X2

[0058] Serial number Model parameter name Physical meaning unit value 1 Jg Gas production index <![CDATA[m 3 / day / MPa]]> 1310 2 Jw Liquid extraction index <![CDATA[m 3 / day / MPa]]> 0.06 3 Pe_init Initial formation pressure MPa 9.214 4 qg_choke Wellhead throttling correction factor Dimensionless 1.131 5 qw_LiquidFilm Wellhead fluid production correction factor Dimensionless 12125 6 param_hl1 Liquid holdup correction factor 1 Dimensionless 3.128 7 param_hl2 Liquid holdup correction factor 2 Dimensionless 0.7489 8 param_dp_hole Horizontal section wellbore pressure drop coefficient Dimensionless 0.125 9 dpwf_init Initial seepage pressure difference MPa 1.5 10 param_divideForOpen Gas-liquid production split coefficient of oil pipe shoe Dimensionless 0.6

[0059] Since the on-site acoustic wave liquid measurement experiment is greatly affected by gas well production: Figure 6 It can be seen that due to the instability of the gas-liquid two-phase flow in the oil pipe, the test data has obvious fluctuations; Figure 7 It can be seen that the liquid level data of the casing annulus is relatively stable. The production history fitting error of Well X2 is shown in Table 3 after comparing various calculation results with the measured values.

[0060] Table 3. Results of error analysis on production history matching of Well X2

[0061] Serial number Data Types Mean absolute error Mean relative error illustrate 1 Oil pressure 0.27MPa 6.65％ Exclude some abnormal oil pressure data 2 Casing Pressure 1.7MPa 24.59％ Exclude some abnormal casing pressure data 3 Gas production <![CDATA[3112m 3 / d]]> 32.65％ none 4 Vertical depth of oil level 86.3m 9.97％ Exclude abnormally small data 5 Casing liquid vertical depth 192.58 7.66％ none

[0062] A continuous production system and different intermittent production systems were generated, and a 30-day forecast was conducted for each production system. The forecast results of different forecasting schemes are shown in Table 4. The schemes were ranked in the order of minimum wellbore liquid accumulation, minimum oil-casing pressure difference, and maximum cumulative gas production. The optimal production system for this well is an intermittent system of 120 minutes on and 120 minutes off.

[0063] Table 4 Prediction results of different prediction schemes

[0064] Serial number Production plan <![CDATA[Cumulative gas production (m 3 )]]> <![CDATA[Average wellbore liquid holdup (m 3 )]]> Average oil-casing pressure difference (MPa) 0 openinf,shut0 23350.282 33.25 1.237 1 open120,shut120 118791.138 32.334 1.149 2 open120,shut360 54360.931 32.339 1.731 3 open120,shut600 35723.207 32.416 1.832 4 open360,shut120 55218.799 33.714 1.753 5 open360,shut360 106940.003 31.674 1.355 6 open360,shut600 80454.904 32.487 1.592 7 open600,shut120 32986.388 36.703 1.862 8 open600,shut360 25125.685 31.192 1.282 9 open600,shut600 52193.916 36.605 1.797

[0065] The above description is not intended to impose any form of limitation on the present invention. Although the present invention has been disclosed through the above embodiments, it is not intended to limit the present invention. Any technician familiar with the profession can make some changes or modifications to equivalent embodiments of equivalent changes using the technical contents disclosed above without departing from the scope of the technical solution of the present invention. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solution of the present invention still falls within the scope of the technical solution of the present invention.

Claims

1. A method for predicting liquid loading in horizontal wells based on gas well production data, characterized in that: The following steps are involved: S1, obtaining static data and instantaneous data of the target gas well; S2, preprocess the data; S3, fitting the production history of the gas well and analyzing the current production status of the gas well; S4, predicting the production performance of the gas well in the future and selecting the optimal production system of the target gas well; wherein the detailed steps of step S3 are: S31, calculating the wellbore fluid loading state at the initial moment; S32, construct a priori fitting process pool with high individual number and low evolutionary generation, simulate and evaluate production history of different parameter groups, and screen feasible solution groups; S33, construct a posterior fitting process pool with a moderate number of individuals and a moderate number of evolutionary generations, combine the prior fitting results, simulate and evaluate the production history of different parameter groups, and select the optimal solution.

2. A method for predicting liquid loading in horizontal wells based on gas well production data according to claim 1, characterized in that: The static data includes wellbore structure, fracturing data, and formation properties; the instantaneous data includes date, valve status, oil pressure, casing pressure, and instantaneous gas volume.

3. A method for predicting liquid loading in horizontal wells based on gas well production data according to claim 1, characterized in that: The detailed steps of step S4 are: S41, generate different spacing schemes and predict the production performance of different schemes; S42, counting the amount of liquid accumulation in the wellbore, the relationship between the change of oil and casing pressure, the gas production and the water production corresponding to different spacing schemes; S43, screening the optimal production system.

Images