TWI734029B - Production capacity estimation method for geothermal well - Google Patents

Abstract

Embodiments disclose a production capacity estimation method for a geothermal well. The method includes: A. building a terrestrial heat storage level experiment system and obtaining a heat-retrieval data resulting from a heat-retrieval experiment; B. fitting the heat-retrieval data to a pore space heat-retrieval model, generating simulated physical properties in the environment such as pore space and fracture, and creating a terrestrial-heat retrieval model; C. scaling up a pipe flow model by the terrestrial heat storage level experiment system to create a single-well heat-retrieval model, receiving terrestrial heat production capacity data sets at different points in time, creating a value of total heat resistance, and identifying the difference there between for modifications of the single-well heat-retrieval model; and D. optimizing heat-retrieval data computed based on the single-well heat-retrieval model by using an optimization method.

Description

Method for predicting productivity of geothermal wells

The present invention relates to a method that can be used to simulate the geothermal productivity of a single well to establish the total thermal resistance of a geothermal well, so that the optimal heat extraction estimation method can be calculated.

Due to the current shortage of energy on the earth, the search for a renewable energy system has become the most important issue without delay. Renewable energy is a kind of restorative energy that can be continuously supplemented and reused. The most representative ones are solar energy, wind energy, ocean energy, biomass energy, geothermal energy, etc. As the development technology matures, geothermal energy is not as good as other energy sources in terms of cost recovery, power generation efficiency, and electricity storage capacity, and it must overcome the limitations of many terrain and rock formations, but it is more sustainable than other energy sources and can be recycled and used at the same time. It can reduce carbon emissions and create many opportunities for the geothermal industry.

Therefore, there is the invention No. 201402943 "Single Well, Artesian Geothermal System for Energy Extraction" published by the Republic of China on January 16, 103, which discloses: Provide a renewable energy, single well, primary artesian, geothermal A heat/power generation system that uses heat exchangers or turbines/generators to obtain naturally generated underground geothermal heat to heat water and/or generate mechanical power/electricity. Vacuum insulation can be used, for example, to insulate the underground structure that defines the working fluid flow path to increase system efficiency and ensure a substantially self-generating working fluid flow.

At present, when performing on-site geothermal power generation performance predictions, the productivity of geothermal wells is tested using actual on-site manual tests. The main reason is the lack of effective and reliable models and the lack of knowledge about the thermal and mass transfer characteristics of the reservoir. Therefore, in the application of geothermal heat, it is still necessary to actually mine to Get geothermal heat for supply. Often, the heat extraction efficiency after geothermal extraction is not as good as expected, and the geothermal energy cannot be fully utilized.

Therefore, the applicant of the present invention has applied for the approval of the invention No. I625460 "Best heat extraction system and its establishment method for enhanced geothermal" announced on June 1, 2007, which discloses: the establishment of a numerical model Perform simulated heat extraction. Use the numerical model to obtain a simulation data after simulating the heat. A thermal experiment was performed to verify the correctness of the numerical model. The heat extraction experiment is used to obtain an experimental data, which is compared with the simulated data to verify the correctness of the numerical model. An optimization method is used to obtain one of the best operating conditions in the simulation data. Use the optimal operating conditions to establish an enhanced geothermal optimal heat extraction system. With the input of the average particle size and pressure of the geological pore conditions, the corresponding mass flow rate of the maximum heat extraction can be obtained by querying.

Although the previous patent case can be used to query the average particle size and pressure of different formations in the geothermal reservoir, the mass flow rate of the corresponding maximum heat extraction can be used as a reference for the mass flow rate to obtain the maximum heat extraction. However, it cannot establish the total thermal resistance for each single well in order to estimate the optimal heat extraction, so it has its shortcomings in use.

In view of this, in view of the current construction of geothermal power plants, the production capacity of geothermal wells must be mastered first, and the current production capacity control comes from on-site production testing. On-site production capacity prediction is a mature evaluation method in the economic benefit evaluation of power plants, and relevant cost information is not difficult to obtain. However, one of the most important links in this assessment is the cost of each productivity test and its impact on geothermal wells. The maximum enthalpy value that can be produced by geothermal wells is tested by gas or water injection on site. Each productivity test requires about three Up to four weeks, the estimated expenditure is about 3 million yuan, but only four sets of production values of different wellhead gas test pipe diameters can be obtained. Therefore, it takes a long time, high cost, and poor economic efficiency.

Therefore, the present invention provides a method for predicting the productivity of geothermal wells, which includes: establishing a geothermal reservoir experimental system, and obtaining a heat extraction result through heat extraction experiments. The geothermal reservoir experimental system is A test section is formed by a high-pressure test pipeline to simulate the heat extraction result of a working fluid flowing through a reservoir; use the heat extraction result to fit a pore heat extraction model to complete the pore and fracture conditions The next one is to simulate physical characteristics to establish a geothermal heat extraction model. The geothermal reservoir experimental system uses the heat extraction results, and then uses a computer to simulate the working fluid in the reservoir in proportion to the test section. The heat extraction phenomenon in the reservoir is obtained, and a simulation result is obtained, and the difference between the simulation result and the heat extraction result is compared; the scale of the geothermal reservoir experimental system is used to enlarge the scale, combined with a pipe flow model to simulate the establishment of a single well Thermal model, input a geothermal productivity data at different time points, establish the total thermal resistance of a geothermal well, and compare its differences, so as to modify the single well heat extraction model, and use the geothermal reservoir experimental system in the computer, Combine the pipe flow model and enlarge it to the local scale model to form the single-well heat extraction model, which can be used to evaluate the geothermal system that is actually applied to the site; an optimization method is used to optimize the calculation of the single-well heat extraction model. Heat, using the optimization method to calculate the most suitable injection flow rate of the geothermal well under the known depth of the geothermal well and the state of the pores of the formation, so as to obtain the maximum heat extraction that can be extracted after flowing through the geothermal well.

The above-mentioned heat extraction results include changes in the experimental physical characteristics of the working fluid under different medium particle diameters, pressures, flow rates, porosity, laminar flow conditions or flow rates.

The above-mentioned experimental physical characteristics include one of heat exchange efficiency, heat convection coefficient, heat dispersion, heat extraction, or any combination thereof.

The aforementioned simulated physical properties include one or any combination of thermal diffusivity, effective thermal conductivity, effective specific heat, longitudinal thermal dispersibility, and lateral thermal dispersibility.

Use the above heat extraction results to provide the pore heat extraction model in the computer to fit the heat dispersion under various pressure and flow conditions, and compare it with the heat extraction and the temperature difference between the inlet and outlet measured during the heat extraction experiment Its difference.

As mentioned above, input the geothermal productivity data of any known geothermal well before the time into the single-well heat extraction model to obtain the old total thermal resistance of one of the known geothermal wells, and then input the geothermal productivity data later The single-well heat model is obtained, and a new total thermal resistance of the known geothermal well is obtained, and the new and old total thermal resistance are compared. The difference in thermal resistance is used to modify and update the single-well heat extraction model to obtain a new single-well heat extraction model, and to verify the flow attenuation of the known geothermal well over time.

The above-mentioned geothermal productivity data includes data on the depth, diameter, bottom hole temperature, outlet temperature, outlet pressure and flow rate of the geothermal well.

The geothermal heat extraction model based on the pore heat extraction model jointly established by a Brinkman model and a heat transfer module is fitted by the above computer, and the optimization method is a simple conjugate gradient Law.

The above technical features have the following advantages:

1. A single well heat extraction model with similar productivity can be developed to simulate the productivity test of geothermal wells, so as to reduce the cost and time consumption of the actual productivity test.

2. Using the single-well heat extraction model can predict the current test results of a single well under different simulated physical characteristics, and can be optimized to predict the best productivity results.

3. It is further possible to input a geothermal productivity data at different time points to establish the total thermal resistance of a geothermal well, and obtain the optimal heat extraction under the set formation conditions, thereby improving the heat extraction efficiency.

(1): Test section

(11): Positive displacement pump

(12): Preheating water tank

(13): Flowmeter

(14): Pressure regulating valve

(15): Cooling water tank

(16): Signal line

(17): Data Logger

(18): Computer

(19): Thermocouple

(2): Single well geothermal system

(2A): Double-well geothermal system

(21): Cold working fluid

(22): Hot working fluid

(3): Pore heat model

(31): Entrance

(32): Export

(4): Cylindrical tube

(41): Porous media

[The first figure] is a flowchart of a method for predicting productivity of geothermal wells according to an embodiment of the present invention.

[The second figure] is the configuration diagram of the geothermal reservoir experimental system according to the embodiment of the present invention.

[Third Figure] is a schematic diagram of a geothermal heat extraction model according to an embodiment of the present invention.

[Fourth Figure] is a schematic diagram of a pore heat model according to an embodiment of the present invention.

[Fifth Figure] is a comparison diagram of the temperature difference between the inlet and outlet of the empty tube and the diameter of 1.54mm and 2.03mm as the flow rate changes at 10, 30MPa in the embodiment of the present invention.

[The sixth figure] is a comparison diagram of the temperature difference between the inlet and outlet of the test section of the heat-taking result and the simulation result of the embodiment of the present invention.

[The seventh figure] is a comparison diagram of the heat extraction result of the embodiment of the present invention and the simulation result.

[The eighth figure] is a comparison diagram of the longitudinal and transverse thermal dispersion results of fractured flow and pore flow under different flow rates in the embodiment of the present invention.

[Figure 9] is a schematic diagram of a single well heat extraction model according to an embodiment of the present invention.

Please refer to the first figure. The embodiment of the present invention is a method for predicting the productivity of a geothermal well, which includes the following steps: A. Establish a geothermal reservoir experimental system to obtain a heat extraction result through heat extraction experiments. As shown in the second figure, the geothermal reservoir experimental system is used to conduct heat extraction experiments to obtain the heat extraction results. The geothermal reservoir experimental system is a test section (1) formed by a high-pressure test pipeline, which is filled with different medium particle diameters and changes the relevant pressure and flow parameters of a working fluid to simulate the flow of the working fluid. The heat extraction result of a reservoir. The present invention uses water as the working fluid. Using a positive displacement pump (11) in a constant flow and constant pressure mode, the working fluid is injected into the test section (1) after passing through a preheating tank (12) and a flow meter (13), and then the working flow is then After passing through a pressure regulating valve (14) and a cooling water tank (15), it recirculates back to the positive displacement pump (11). At the same time, the heat extraction efficiency obtained in the test section (1) is transmitted to a data recorder (17) and a computer (18) through a signal line (16). Use this test section (1) to carry out the heat extraction test of the porous medium, so as to obtain the experimental physical characteristics of different medium particle size, flow rate, reservoir pressure and so on. And we can know the difference in the operation process of different porosity, laminar flow state, flow rate and pressure and other experimental physical characteristics. The water pressure, flow rate, and rock diameter are tested during the heat extraction experiment, and the above-mentioned experimental physical characteristics of the working fluid, such as heat exchange efficiency, heat convection coefficient, heat dispersion, and heat extraction, can be obtained. Furthermore, the above-mentioned experimental physical characteristics experiments can be carried out for related parameters such as the water-heat exchange efficiency of different porosities, the pressure loss caused by the pores, and the buoyancy.

The inlet end of the above-mentioned test section (1) is connected to the preheating water tank (12), which can control the temperature of the working fluid when it is injected, and then the cooling water tank (15) is connected to the outlet end of the test section (1), so that The outlet water temperature drops to facilitate system circulation. The purpose of the preheating water tank (12) and the cooling water tank (15) is to enable the positive displacement pump (11) and the pressure regulating valve (14) to operate within the operating temperature, and the test section (1) is The heater inputs a fixed voltage to heat the test section (1) to achieve thermal equilibrium. The test section (1) is filled with different porous media, and 10 sets of thermocouples (19) are installed on the outer wall of the test section (1), which surrounds the test section (1) in a ring The outer wall groove of the test section (1) is then covered with thermal insulation cotton and thermal insulation material to reduce the influence of heat loss. The purpose is to measure the temperature response of the outer wall in steady state and transient state, and then bring the following calculations Formula (1) obtains the temperature change in the wall. The heat extraction experiment is to test the heat transfer characteristics of the working fluid in the pore medium. The heat extraction results are used to calculate the heat convection coefficient and heat exchange amount through the setting of constant flow, pressure and heat flux, and observe its temperature distribution. And use the temperature record of the heat extraction experiment to calculate the temperature distribution of the inner wall of the test section (1), and provide a follow-up model to fit the thermal dispersion setting of the model. The calculation formula (1) is as follows:

T _IW : inner wall temperature (K); T _OW : outer wall temperature (K); Q _r : input heat flux on the surface of the tube (W/m ² ); d ₁ : inner diameter of the test section (m); d ₂ ：The outer diameter of the test section (m); λ _{s u s} ：The thermal conductivity of stainless steel (W/mk).

The heat extraction experiment of the present invention has been completed under the 7.5MPa hollow tube (fracture flow), under the conditions of the quartz sand particle size of 1.54mm and 2.03mm, the flow rate is 0.33g/s, 0.66g/s, 0.99g/s and 1.32g/ s of heat extraction experiment. It is expected that it will continue to be established under the various pressures of 8MPa, 9MPa, 10MPa, 11MPa and 12.5MPa. The same heat extraction results to obtain the complete physical characteristics of the experiment under different pressure, flow and pore conditions. The experimental method for the fracture conditions is the same as the experimental method for the above-mentioned pore conditions.

B. Use the heat extraction results to fit a pore heat extraction model, complete a physical characteristic simulation under the conditions of pores and cracks, and establish a geothermal heat extraction model. It is based on the heat extraction result obtained by the geothermal reservoir experimental system through the heat extraction experiment, and then according to the test section (1) in equal proportions, the computer (18) is used to fit the Brinkman (Brinkman) ] The geothermal heat extraction model based on the model and the pore heat extraction model of the heat transfer module is used for various simulations using the computer (18) to simulate the heat extraction phenomenon of the working fluid in the geothermal reservoir Physical characteristics. The simulated physical properties include providing the pore heat model in the computer (18) with the heat extraction result to fit the heat dispersion under various pressure and flow conditions, which is measured by the heat extraction experiment process The difference between the measured heat extraction and the heat dispersion of the temperature difference between the inlet and outlet is compared and modified to fit a more accurate pore heat extraction model to establish the geothermal heat extraction model.

Please refer to the third figure, whether it is a single-well geothermal system (2) that simultaneously provides cold working fluid (21) input and hot working fluid (22) output; or one of the wells can provide a cold working fluid ( 21) A dual-well geothermal system (2A) with input and another well available for output of thermal working fluid (22). The heat extraction modes using water as the working fluid are all related to pore heat transfer, so the pore heat extraction model (3) is used as a reasonable geothermal heat extraction model. The pore heat extraction model (3) is used in the computer (18) to explore the relationship between the heat transfer of the working fluid in the porous medium, and the geothermal heat extraction model constructed by it is set as the flow field and the temperature field The coupling type of the two models, its mathematical models include continuous equation, momentum balance equation, medium energy balance equation and fluid energy balance equation, etc.

The calculation formula (2) of the continuous equation is as follows:

The calculation formula (3) of the momentum balance equation is as follows:

The governing equation of heat transfer in porous media is obtained by the average volume method, so that the pores and the media can individually obtain the inherent average value. The heat transfer balance equation between the medium and the fluid has been widely used in the analysis of porous media The heat transfer. Therefore, Fourier's Law is used to describe media and fluids that are completely saturated in porous media. The calculation formula (4) of the medium energy balance equation is as follows:

The calculation formula (5) of the fluid energy balance equation is as follows:

In the equation, ρ is the density, u is the flow velocity of the fluid, Q _br is the mass force, ε is the porosity, μ is the dynamic viscosity of the fluid, K is the permeability, F is the force term, C _p is the atmospheric specific heat, and k is Thermal conductivity. As shown in the fourth figure, in the geothermal heat extraction model, the surface of the pore heat extraction model (3) is set as a constant heat source to heat its interior, and one end is provided with an inlet (31) for cooling the working fluid (21) ) Input, the other end is provided with an outlet (32) for the output of the hot working fluid (22) after being added. Change the inlet flow rate, system pressure and internal medium particle size respectively, and observe the changes of outlet temperature and inner wall temperature. Using the pore heat extraction model (3), after injecting fluid in a closed area that can be computer-simulated into the reservoir, the pore heat extraction model (3) calculates the heat extraction phenomenon and obtains a simulation result. The simulation result to be compared is the calorific value of the fluid, and the temperature recorded by the thermal experiment is used to calculate the overall temperature distribution along the reservoir, and compare it with the simulation result of the pore thermal model (3). The calculation formula is as follows:

After testing the simulation results under different pressures, it has been determined that the geothermal reservoir experimental system is sufficient to simulate the phenomenon of underground reservoirs at a depth of 2-3 thousand meters. As shown in the fifth figure, the system is 10, 30MPa The temperature difference between the inlet and outlet of the lower hollow tube and the diameter of 1.54mm and 2.03mm with flow changes. First use the built-in water properties as the geothermal heat extraction model to explore the conditions under 10MPa and 30MPa. The simulation results show that the pressure has minimal effect on heat extraction, which proves that the test section (1) established with the geothermal reservoir experimental system at about 100 atmospheres is sufficient to simulate the condition of the underground reservoir. It has also been observed that the influence of pore changes is also small, but there are significant differences in the influence of empty pipes (fracture flow) and pore conditions. The flow rate is the most important influence on heat extraction. It can be seen that in the future, heat extraction experiments and computer simulations will be carried out in the heat extraction experiments and computer simulations to conduct further heat extraction analysis for empty pipes at different pressures around 10MPa and different porosity conditions.

The thermal diffusion property of the geothermal heat extraction model is an empirical value, and the similar thermal convection coefficient must be obtained by experiment. Therefore, the present invention uses the heat extraction results of the heat extraction experiment to fit the pore heat extraction model to establish a reasonable geothermal heat extraction model for this reason. The definition of simulated physical properties in pore heat transfer is as follows.

。 Thermal diffusivity:

.

Effective thermal conductivity:

Effective specific heat: C _peff = ε. ρ _f . C _p,f +(1- ε ) . ρ _p . C _p,p .

。 Longitudinal thermal dispersion:

.

。 Transverse thermal dispersion:

.

Because the present invention has completed the establishment of the geothermal heat extraction model in the case of an empty tube with a particle size of 1.54mm and 2.03mm. The established experimental system for the geothermal reservoir has also completed the heat extraction experiments with flow rates of 0.33g/s, 0.66g/s, 0.99g/s and 1.32g/s under 7.5MPa empty pipe conditions. The thermal result is fitted to the simulation result of the geothermal thermal model to obtain the thermal diffusivity under this condition. By adjusting the longitudinal and lateral heat dispersion (λ _lo and λ _tr ) in the geothermal heat extraction model, the computer simulation of the geothermal heat extraction model can be fitted to the heat extraction results of the heat extraction experiment, and be combined with the heat extraction experiment Compare the temperature difference between the entrance and exit and the amount of heat to confirm the correctness of the computer simulation. After verification, it was found that the temperature difference between the entrance and exit of the test section was compared with the simulation result of the computer simulation, and the average error value was less than 0.001%. The comparison between the heat extraction result and the simulation result is shown in the seventh figure, and the average error value is less than 0.72%. From the above comparison results, it can be seen that the simulation results are consistent with the heat extraction results, so the correct heat dispersion can be established.

As shown in the sixth and seventh graphs, it is shown that the mass flow rate increases, which increases the heat extraction, but causes the temperature difference between the inlet and outlet of the test section to decrease. Therefore, as the mass flow rate increases, the heat extraction has a predictable approach value. Therefore, in the actual geothermal single well, there will be an appropriate outlet flow rate to obtain a maximum heat extraction. And found that in terms of thermal dispersibility, as shown in Figure 8, according to the _{definition of thermal dispersibility (k disp} ), flow velocity, specific heat, and dispersibility all affect the thermal diffusivity. But the flow velocity is a square term, so the dispersion of pore heat transfer at low flow velocity is an influential factor. At this time, the pore heat transfer effect is greater than the heat transfer effect of the fractured flow (empty pipe). However, when the flow rate increases, the influence of dispersion decreases rapidly, so when the flow rate increases, the heat transfer of the empty tube is better than the flow of the pore heat transfer. In other words, low flow rates have better heat extraction effects under pore conditions, but high flow rates have better fracture conditions.

C. Use the geothermal reservoir experimental system to enlarge the scale and combine a pipe flow model to establish a single well heat extraction model, input a geothermal productivity data at different time points, establish the total thermal resistance of a geothermal well, and compare it The difference is used to modify the single-well heat extraction model. The computer is a geothermal reservoir experimental system fitted with the above-mentioned heat extraction experiment, combined with the pipe flow model to enlarge it to an on-site scale model, for the actual application of the on-site geothermal system for evaluation. Due to the complex geological structure, characteristics, physical and chemical conditions of each site, the geothermal reservoir structure at different geothermal sites is different. The establishment of a reasonable in-situ value of the single-well heat extraction model will help to understand the underground heat flow phenomenon and various operating parameter settings, further predict the total geothermal power generation capacity, and optimize the heat extraction of geothermal wells. The combination of the present invention The pipe flow model is based on the size of a single well in the Qingshui area of Yilan County, and is used as an on-site heat extraction model for the single well. The scale-up of the single-well heat extraction model for in-situ heat extraction is to establish the single-well heat extraction model in accordance with the current size and depth of the clean-water in-situ sinking well and the productivity test around 2008. Then adjust the injected flow rate to maximize heat extraction. Based on the results of the enlarged numerical model, the scale of the on-site heat extraction can be predicted, so as to have a reference basis when evaluating the cost. The geometric design of the single-well heat extraction model, as shown in the ninth figure, is to form the tube flow model with a hollow cylindrical tube (4), and fill the bottom part with a porous medium (41). The porous medium (41) simulates the state of the reservoir, and adjusts the injection flow rate to maximize the heat extraction plan to connect to the next heat extraction evaluation.

Then through the computer, input the geothermal productivity data of an on-site geothermal well in Qingshui area from 2008 to 2009 into the single-well heat extraction model to obtain the old total thermal resistance of one of the geothermal wells (the entire single well is regarded as a system ). Then, input the on-site geothermal productivity data of the geothermal well in Qingshui area in 2017 into the single-well heat extraction model to obtain a new total thermal resistance. The calculation of the new and old total thermal resistance is obtained by dividing the heat loss of the geothermal well by (the difference between the well temperature and the ambient temperature). Compare the difference between the new and old total thermal resistances (the new and old total thermal resistances are subtracted and divided by the percentage of the value from 2008 to 2009) to modify and update the single-well heat extraction model to obtain the new single-well extraction model. Thermal model and verify the flow attenuation of the geothermal well over time. The geothermal productivity data at different time points includes data on the depth, diameter, bottom hole temperature, outlet temperature, outlet pressure and flow rate of the geothermal well. By modifying the single-well heat extraction model, the future productivity test results of different pipe diameters can be predicted, and the best productivity results can be predicted in the single-well heat extraction model. The surface of the single-well heat extraction model is set as a heat dissipation surface fitted by the on-site productivity test. Respectively change the inlet flow rate and system pressure, and then observe the outlet temperature and inner wall temperature changes to fit the known productivity test, and further obtain the estimated heat loss of the well wall to obtain the total thermal resistance of the geothermal well to obtain Heat extraction and efficiency under set formation conditions.

D. Use an optimization method to calculate the optimal heat extraction of the single well heat extraction model. The optimization method is used to calculate the most suitable injection flow rate of a geothermal well under the known depth and pore state of the formation, so as to obtain the maximum heat extraction that can be extracted after flowing through the geothermal well, and at the same time achieve the maximum The best outlet temperature range. Only need to optimize the design for the initial module, you can obtain an optimized module, and at the same time obtain relatively optimized operating parameters. The optimization method used repetitively simulates with a similar capacity test model through the optimization process to find the best design. To optimize model working conditions and design parameters. The optimization method used is the Simplified Conjugated Gradient Method (SCGM), which will help sensitivity analysis when calculating the objective function. First, make an initial guess for each design parameter, and then evaluate the conjugate gradient coefficient and search direction in the iterative process, and continuously update the design parameters according to this process. When the objective function meets the design requirements, the optimization process is completed. The main calculation formula is as follows:

Where a _k is the design parameter, β _k is the step distance _{of the iterative search, and ξ k} is the direction of the iterative search. The direction of the iterative search is the fastest descent direction of the linear combination, which is related to the gradient of the objective function. The calculation formula is as follows:

Where J is the objective function and r is the conjugate gradient coefficient.

The formula for calculating the conjugate gradient coefficient is as follows:

Among them, the step distance β _k can be assumed to be a fixed value, β _k =C, k =1, 2..., n .

Among them, the size of the C value is determined according to the optimization problem set. The steps of the simple conjugate gradient method are as follows.

a. Define the design parameters a _k , k =1, 2..., n .

b. Use multiple physical quantity software to calculate the objective function J. When the objective function meets the design requirements, the solution process will be terminated, otherwise skip to step c.

c. Perform numerical sensitivity analysis, first give a perturbation value of △ a _k. Then calculate the ratio △ J obtained by the change of the objective function, and then directly calculate the numerical differentiation of the gradient function in each design parameter as shown below:

Use the above calculation formulas (6), (7) and (8) to calculate the conjugate gradient coefficient r and the conjugate gradient search direction ξ _{k of} each design parameter a _k , k =1, 2, ..., n.

d. Give steps β _k for all design parameters, k =1, 2, ..., n .

e. Use the calculation formula (6) to update all the design parameters of the next iteration, and then return to step b.

After the numerical results are optimized, use numerical simulation to establish a new single-well heat extraction model, and then perform heat exchange analysis of optimized operating conditions based on the new single-well heat extraction model to calculate the optimal Heat.

Based on the description of the above embodiments, when one can fully understand the operation and use of the present invention and the effects of the present invention, but the above embodiments are only the preferred embodiments of the present invention, and the implementation of the present invention cannot be limited by this. The scope, that is, simple equivalent changes and modifications made according to the scope of the patent application of the present invention and the content of the description of the invention, are all within the scope of the present invention.

Claims (8)

A method for predicting the productivity of geothermal wells, including: establishing a geothermal reservoir experimental system, and obtaining a heat extraction result through heat extraction experiments. The geothermal reservoir experimental system is formed by a high-pressure test pipeline. The test section is used to simulate the heat extraction result of a working fluid flowing through a reservoir; use the heat extraction result to fit a pore heat extraction model to complete a simulated physical characteristic under pore and fracture conditions, and establish a geothermal extraction The thermal model is based on the geothermal reservoir experimental system through the heat extraction results, and then according to the test section in equal proportions, using a computer to simulate the heat extraction phenomenon of the working fluid in the reservoir, and obtain a Simulation results, compare the difference between the simulation results and the heat extraction results; use the geothermal reservoir experimental system to enlarge the scale, and combine a pipe flow model to simulate a single well heat extraction model, and input a geothermal productivity at different time points Data, establish the total thermal resistance of a geothermal well, and compare its differences, so as to modify the single-well heat extraction model, use the geothermal reservoir experimental system in the computer, and combine the pipe flow model to enlarge it to a local scale model. In order to form the single-well heat extraction model, it can be used to evaluate the geothermal system that is actually applied in the field; an optimization method is used to optimize the single-well heat extraction model to calculate the optimal heat extraction, and the optimization method is used to calculate the Knowing the depth of the geothermal well and the pore state of the formation, and the most suitable injection flow rate of the geothermal well, to obtain the maximum heat extraction that can be extracted after flowing through the geothermal well. For example, the method for predicting the productivity of geothermal wells described in the scope of the patent application, wherein the heat extraction results include the experimental physics of the working fluid under different medium particle size, pressure, flow rate, porosity, laminar flow state or flow velocity. Changes in characteristics. For example, the method for predicting the productivity of geothermal wells described in item 2 of the scope of patent application, wherein the experimental physical characteristics include one of heat exchange efficiency, heat convection coefficient, heat dispersion, heat extraction, or any combination thereof. For example, the method for predicting the productivity of geothermal wells described in the scope of the patent application, wherein the simulated physical properties include thermal dispersibility, thermal diffusivity, effective thermal conductivity, effective specific heat, vertical thermal dispersibility, and lateral thermal dispersibility. One or any combination thereof. For example, the method for predicting the productivity of geothermal wells described in the scope of the patent application, wherein the heat extraction result is used to provide the pore heat extraction model in the computer to fit the heat dispersion under various pressure and flow conditions, And compare the difference with the heat extraction and the temperature difference between the inlet and outlet measured during the heat extraction experiment. For example, the method for predicting the productivity of geothermal wells described in the scope of the patent application, wherein the geothermal productivity data of any known geothermal well before the time is input into the single-well heat extraction model to obtain the known geothermal well An old total thermal resistance, and then input the time later the geothermal productivity data into the single-well heat extraction model to obtain a new total thermal resistance of the known geothermal well, and compare the difference between the new and the old total thermal resistance, In this way, the single-well heat extraction model is revised and updated to obtain a new single-well heat extraction model and to verify the flow attenuation of the known geothermal well over time. For example, the geothermal well productivity estimation method described in item 6 of the scope of patent application, wherein the geothermal productivity data includes data on the depth, diameter, bottom hole temperature, outlet temperature, outlet pressure and flow rate of the geothermal well. For example, the method for predicting the productivity of geothermal wells described in the scope of the patent application, wherein the computer is used to fit the pore heat extraction model jointly established by a Brinkman model and a heat transfer module as the basis The geothermal heat extraction model, and the optimization method is a simple conjugate gradient method.

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