TW202012780A - 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

Geothermal well productivity prediction method and system

The invention relates to a method and system 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 can be calculated.

Due to the current shortage of energy on the planet, the search for renewable energy systems has become a primary issue that cannot be delayed. Renewable energy is a kind of recoverable energy, which can be continuously replenished and reused. The most representative ones are solar energy, wind energy, ocean energy, biomass energy, and geothermal energy. As the development technology becomes more and more mature, although geothermal energy is not as good as other energy sources in cost recovery, power generation efficiency and storage capacity, and must overcome the limitations of many terrain rock layers, it is more sustainable than other energy sources. It can reduce carbon emissions and create many opportunities in the geothermal industry.

Therefore, there is the patent case of the invention No. 201402943 published on January 16, 103, "Single Well, Gravity Geothermal System for Energy Exploitation", which discloses: providing a renewable energy source, single well, primary gravity, geothermal A heat/electricity generation system that uses heat exchangers or turbines/generators to capture naturally generated underground geothermal heat to heat water and/or generate mechanical force/electricity. Vacuum insulation can be used, such as to insulate the underground structure that defines the working fluid flow path, to increase system efficiency and ensure substantially self-generated working fluid flow.

As the current geothermal power generation performance predictions are performed, the geothermal well production capacity is tested using the actual manual testing in the field. The main reasons are the lack of effective and reliable models and the lack of knowledge about the reservoir heat and mass transfer characteristics. Therefore, in the application of geothermal energy, it still needs to be actually mined to obtain geothermal energy for supply. Often the heat extraction efficiency after geothermal mining is not as expected, and the geothermal energy cannot be fully utilized.

Therefore, the applicant of the present invention has applied for and approved the patent case of the invention No. I625460 "Optimal heat extraction system for enhanced geothermal heat and its establishment method" published on June 1, 107, which was disclosed as: establishing a numerical model Perform simulated heat extraction. Use the numerical model to obtain simulated data after simulating heat extraction. Through a heat extraction experiment to verify the correctness of the numerical model. Use the heat extraction experiment to obtain experimental data, and compare with the simulated data to verify the correctness of the numerical model. An optimized method is used to obtain an optimal operating condition in the simulation data. The optimal operating conditions are used to establish an optimal geothermal heat extraction system. The average particle size and pressure that can be used to input geological pore conditions can be queried to find out the corresponding mass flow rate of the maximum heat extraction.

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

Therefore, in view of the current construction of geothermal power plants, it is necessary to master the production capacity of geothermal wells, and the current production capacity is derived from the on-site production capacity test. The on-site capacity forecast 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 the impact on the geothermal well. The maximum enthalpy value that can be produced by the geothermal well tested by gas injection or water injection in the local area. Each productivity test requires about three The budget is estimated to be about three million to four weeks, but it can only obtain the production value of four groups of different wellhead gas test pipe diameters. Therefore, it takes a long time, high cost and poor economic benefit.

Therefore, the present invention provides a method for predicting the productivity of a geothermal well, which includes: establishing a geothermal reservoir experiment system to obtain a heat extraction result through a heat extraction experiment. The geothermal reservoir experiment system uses a high-pressure test pipeline A test section is formed to simulate the heat extraction result of a working fluid flowing through a reservoir; the heat extraction result is used to fit a pore heat extraction model to complete a simulated physical characteristic under pore and fracture conditions, Establishing a geothermal heat extraction model, using the geothermal reservoir experimental system to pass the heat extraction results, and then using the computer to simulate the heat extraction of the working fluid in the reservoir in a proportional manner according to the test section And obtain a simulation result, compare the difference between the simulation result and the heat extraction result; use the geothermal reservoir experimental system to enlarge the scale, and combine a pipe flow model simulation to establish a single well heat extraction model, enter different time points Data of a geothermal productivity, establish the total thermal resistance of a geothermal well, and compare the differences, so as to modify the single-well heat extraction model, and use the geothermal reservoir experimental system in the computer to enlarge to the pipe flow model to Local scale model to form the single-well heat extraction model for practical application to the local geothermal system for evaluation; use an optimization method to make the single-well heat extraction model calculate the optimal heat extraction and use the optimization The method calculates the injection rate of the most suitable geothermal well under the known geothermal well depth and rock formation pore state, so as to obtain the maximum heat extractable after flowing through the geothermal well.

The above heat extraction results include changes in the experimental physical characteristics of the working fluid under different medium particle sizes, pressures, flow rates, porosities, laminar flow states or flow rates.

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

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

Based on the above heat extraction results, the pore heat extraction model in the computer is fitted to the heat dispersibility under various pressure and flow conditions, and compared with the heat extraction measured during the heat extraction experiment and the temperature difference between the inlet and outlet Its differences.

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

The above 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 invention is also a geothermal well productivity estimation system, which is established by the above geothermal well productivity estimation method.

The above technical features have the following advantages:

1. A single-well heat extraction model with a similar capacity that simulates geothermal well productivity testing can be developed to reduce the cost and time of actual productivity testing.

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

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

Referring to the first figure, an embodiment of the present invention is a method for estimating the productivity of a geothermal well, which includes the following steps:

A. Establish a geothermal reservoir experiment system, and obtain a heat extraction result through the heat extraction experiment. As shown in the second figure, the geothermal reservoir experimental system is used to perform heat extraction experiments to obtain the heat extraction results. The experimental system of the geothermal reservoir is formed by a test section (1) formed by a high-pressure test pipeline, which is filled with different medium particle sizes and changes the parameters such as the pressure and flow rate of a working fluid to simulate the working fluid flow The heat extraction result after a reservoir. The present invention uses water as the working fluid. Using a positive displacement pump (11) in a constant flow and pressure mode, the working fluid is injected into the test section (1) after passing through a preheating water tank (12) and a flow meter (13), and then the working flow rate After passing through a pressure regulating valve (14) and a cooling water tank (15), it is recirculated back to the positive displacement pump (11). At the same time, the heat extraction efficiency obtained by the test section (1) is transmitted to a data recorder (17) and a computer (18) through a signal line (16). The test section (1) is used to perform 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, etc. In addition, the differences in experimental physical characteristics such as different porosity, laminar flow state, flow velocity, and pressure during operation can be known. During the heat extraction experiment, the water pressure, flow rate and rock formation particle size are tested to obtain the above-mentioned experimental physical characteristics of the working fluid, such as heat exchange efficiency, heat convection coefficient, heat dispersion and heat extraction. Further experiments on the physical properties of the above experiments can be carried out for the relevant parameters of water and heat exchange efficiency at different porosities, pressure loss and buoyancy caused by the pores.

The inlet end of the test section (1) is connected to the preheating water tank (12) to control the temperature of the working fluid during injection, and 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) and The heater inputs a fixed voltage to heat the test section (1) to achieve thermal equilibrium. The test section (1) is filled with different pore media, and 10 sets of thermocouples (19) are installed on the outer wall of the test section (1). The outer wall groove of the test section (1) is covered with thermal insulation cotton and thermal insulation material to reduce the influence of heat loss. Its purpose is to measure the temperature response of the outer wall during steady state and transient state, and then bring into the following calculation 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 porous medium. The heat extraction results are calculated by setting the constant flow rate, pressure and heat flux to calculate the heat convection coefficient and heat exchange amount, and observe the 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 test section (m);

d ₂ : outer diameter of 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 condition of 7.5MPa down tube (fracture flow), quartz sand particle size 1.54mm and 2.03mm, and the flow rate is 0.33g/s, 0.66g/s, 0.99g/s and 1.32g/ s heat extraction experiment. It is expected that the different heat extraction results under various pressures of 8 MPa, 9 MPa, 10 MPa, 11 MPa and 12.5 MPa should be continuously established to obtain the complete physical characteristics of the experiment under different pressures, flow rates and pore conditions. The experimental method for crack conditions is the same as the experimental method for pore conditions.

B. Use this heat extraction result to fit a pore heat extraction model, complete a simulation of physical characteristics 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 experiment system through the heat extraction experiment, and then is proportionally fitted by the computer (18) to Brinkman according to the test section (1). 〕The model and the thermal model of the heat transfer module as the basis of the geothermal model, for the use of the computer (18) computer simulation of the working fluid in the geothermal reservoir of various simulations Physical characteristics. The simulated physical characteristics include providing the pore heat extraction model in the computer (18) with the heat extraction results to fit the heat dispersibility under various pressure and flow conditions, measured by the heat extraction experiment process The difference between the measured heat extraction and the heat dissipation of the temperature difference between the inlet and the outlet is compared and modified to fit the pore heat extraction model more accurately to establish the geothermal heat extraction model.

Please refer to the third figure, whether it is a single-well geothermal system (2) that can simultaneously supply 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 for hot working fluid (22) output. The heat extraction modes using water as the working fluid are all related to the 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 a computer simulation in the computer (18) to discuss the relationship between the heat transfer of the working fluid in the porous medium. The geothermal heat model constructed by it is set to flow field and temperature field. The coupling model of the two models, the mathematical model includes continuous equations, momentum balance equations, medium energy balance equations and fluid energy balance equations.

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

。

.

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

。

.

The dominant equation of heat transfer in porous media is obtained by the average volume method, so that the inherent average value can be obtained between the pores and the medium. The heat transfer balance equation between the medium and the fluid has been widely used to analyze porous media Heat spread. Therefore, Fourier's Law is used to describe the fully saturated medium and fluid 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 fluid's dynamic viscosity, κ 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 heating model, the surface of the pore heating model (3) is set to a constant heat source to heat its interior, and an inlet (31) is provided at one end for cooling the working fluid (21 ) Input, the other end is provided with an outlet (32) for the output of hot working fluid (22) after addition. Change the inlet flow rate, system pressure and internal medium particle size respectively, and observe the changes in outlet temperature and inner wall temperature. Through the pore heating model (3), after the fluid in a closed area can be computer-simulated injected into the reservoir, the phenomenon of heat extraction is calculated through the pore heating model (3), and a simulation result is obtained. The simulation result to be compared is the heat extraction of the fluid, and the temperature recorded in the heat extraction experiment is used to calculate the overall temperature distribution along the reservoir and compared with the simulation results of the pore heat extraction model (3). The calculation formula is as follows:

。

.

Through testing the simulation results under different pressures, it has been determined that the geothermal reservoir experimental system is sufficient to simulate the phenomenon of 2-3 thousand meters deep underground reservoirs. As shown in the fifth figure, it is the temperature difference between the inlet and the outlet of the empty tube at 10 and 30 MPa and the particle size of 1.54 mm and 2.03 mm as the flow rate changes. First use the built-in water properties as the geothermal heat extraction model to discuss the conditions under 10MPa and 30MPa. The simulation results can observe that the pressure has little effect on heat extraction, which can prove that the test section (1) established by the geothermal reservoir experimental system at about 100 atmospheres is sufficient to simulate the condition of the underground reservoir. It is also observed that the effect of pore changes is also small, but the effect of empty tubes (fracture flow) and pore conditions is significantly different. The flow rate has the most important effect on heat extraction. It can be seen that in the future, heat flow analysis of different flow rates of empty tubes and different porosity conditions at different pressures around 10 MPa can be further conducted in the heat extraction experiment and computer simulation.

The thermal diffusion property of the geothermal heat extraction model is an empirical value, and similar thermal convection coefficients must be obtained through experiments. Therefore, the present invention uses the heat extraction result 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:

.

。Longitudinal heat dispersion:

.

。Lateral heat dispersion:

.

Since the invention has completed the establishment of the geothermal heat extraction model in the case of an empty tube with a particle size of 1.54 mm and 2.03 mm. The established geothermal reservoir experimental system has also completed heat extraction experiments with flow rates of 0.33g/s, 0.66g/s, 0.99g/s and 1.32g/s under the condition of 7.5MPa empty pipe, and then the The heat extraction result fits the simulation result of the geothermal heat extraction model to obtain the thermal diffusion coefficient under this condition. By adjusting the longitudinal and lateral heat dissipation ( λ _lo and λ _tr ) in the geothermal heating model, the computer simulation of the geothermal heating model can be fitted to the heat extraction results of the heat extraction experiment and the heat extraction experiment Compare the temperature difference between entrance and exit and extract heat to confirm the accuracy of computer simulation. After verification, it is found that the heat extraction results of the heat extraction experiment and the computer simulation results are shown in the sixth figure, and the average error value of the test section at the entrance and exit is 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 dispersibility can be established.

As shown in the sixth and seventh figures, it shows that increasing the mass flow rate increases the heat extraction, but causes the temperature difference between the entrance and exit of the test section to decrease. Therefore, as the mass flow rate increases, the heat extraction has a predictable approach value. Therefore, there will be an appropriate outlet flow rate in the actual geothermal single well to obtain a maximum heat extraction. And it was found that in terms of thermal dispersibility, as shown in the eighth figure, according to the definition of thermal dispersibility (k _disp ), flow velocity, specific heat and dispersibility all have an effect on the thermal diffusivity. However, the flow velocity is a square term, so the dispersion of pore heat transfer is an influential factor at low flow rates. At this time, the pore heat transfer effect is greater than the fissure flow (empty tube) heat transfer effect. However, when the flow rate increases, the effect of dispersion decreases rapidly. Therefore, after the flow rate increases, the heat transfer of the empty tube is better than that of the pore heat transfer. That is to say, the effect of heat extraction under pore conditions is better at low flow rates, but the crack conditions are better at high flow rates.

C. Use the geothermal reservoir experimental system to enlarge the scale and combine with a tube flow model to establish a single-well heat extraction model, input the data of a geothermal productivity at different time points, establish the total thermal resistance of a geothermal well, and compare them The difference can be used to modify the single well heating model. In this computer, a geothermal reservoir experiment system fitted with the above heat extraction experiment is used, and the tube flow model is enlarged to the local scale model for the actual application of the local geothermal system for evaluation. Due to the complexity of the geological structure, characteristics, physical and chemical conditions of each location, the geothermal reservoir structures located in different geothermal sites are different. Establishing a reasonable in-situ numerical heat extraction model for this single well 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 acquisition of the geothermal well. The tube flow model combined with the present invention is based on the size of a single well in the Qingshui area of Yilan County, and is used as the current single-well heat extraction model. The scale of the single-well heat extraction model for on-site heat extraction is to build the single-well heat extraction model based on the size, depth and production capacity of the current clean water on-site drilling. Then adjust the injection flow to maximize heat. From the enlarged numerical model results, it is possible to anticipate the heat extraction scale of the local heat extraction, so as to have a reference basis when evaluating the cost. The geometric design of the single-well heat extraction model is shown in the ninth figure. A hollow cylindrical tube (4) is used to form the tube flow model, and a porous medium (41) is filled at the bottom of the tube. Porous medium (41) simulates the state of the reservoir, and the adjusted injection flow rate will be planned to maximize heat extraction to connect with the next heat assessment.

Then through this computer, the geothermal productivity data of a local geothermal well in Qingshui area from 2008 to 2009 is input into the single-well heating model, so as to obtain the old total thermal resistance of one of the geothermal wells (taking the overall single well as a system ). Then, the current geothermal productivity data of the geothermal well in the Qingshui area in 2017 was imported into the single-well heating model to obtain a new total thermal resistance. The calculation of the new and old total thermal resistance can be obtained by dividing the heat dissipation 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 then divided by the percentage of 2008 to 2009) to correct and update the single-well heating model to obtain the new single-well sampling 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 revising the single-well heating model, it can predict the future capacity test results of different pipe diameters, and can predict the best productivity results in the single-well heating model. The surface using the single-well heat extraction model is set as a heat dissipation surface fitted by the local productivity test. Change the inlet flow rate and the system pressure separately, and then observe the changes in the outlet temperature and the inner wall temperature, and fit it with the known productivity test to 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 make the single-well heat extraction model calculate the optimized heat extraction. The optimization method is used to calculate the most suitable injection rate of the geothermal well under a known geothermal well depth and rock formation pore state to obtain the maximum heat extraction that can be extracted after flowing through the geothermal well and at the same time to achieve the maximum The best outlet temperature range. Only by optimizing the initial module, an optimized module can be obtained, and the relative optimized operating parameters can be obtained. The optimization method used is repeatedly simulated by a similar capacity test model through the optimization process to find the best design. To optimize the model working conditions and design parameters. The optimization method used is the Simplified Conjugated Gradient Method (SCGM). This simple conjugated gradient method will help sensitivity analysis when calculating the objective function. First make an initial guess for each design parameter, and secondly evaluate the conjugate gradient coefficient and search direction during the iteration 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:

(六)。

(six).

Among them, 𝑎 _𝑘 is the design parameter, 𝛽 _𝑘 is the step of iterative search, and 𝜉 _𝑘 is the direction of iterative search. The direction of the iterative search is the direction of the steepest descent of the linear combination, which is related to the gradient of the objective function. The calculation formula is as follows:

(七)。

(Seven).

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

The calculation formula of the conjugate gradient coefficient is as follows:

(八)。

(Eight).

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. To perform numerical sensitivity analysis, first give a disturbance value of Δ𝑎 _𝑘 . Then calculate the ratio Δ𝐽 of the change of the objective function, and then you can directly calculate the numerical differentiation of the gradient function in each design parameter as follows:

。

.

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

d. For all design parameters, give the step 𝛽 _𝑘 , 𝑘=1, 2, …, 𝑛.

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

After the numerical results are optimized, the new single-well heat extraction model is established by numerical simulation, and then the heat exchange analysis of the optimized operating conditions is performed according to the new single-well heat extraction model to calculate the optimized optimization Heat.

The invention can be a geothermal well productivity estimation system, which is established by the above geothermal well productivity estimation method. It can be used for the simulation of similar productivity of geothermal wells, so as to reduce the cost and time of actual productivity testing. It is easy to connect with models such as power plant design and unit selection to become a model for predicting the power generation scale of geothermal power plants, and further put forward the prediction of the power generation potential of each single well.

Based on the description of the above embodiments, the operation, use and effects of the present invention can be fully understood. However, the above-mentioned embodiments are only preferred embodiments of the present invention, and cannot be used to limit the implementation of the present invention. The scope, that is, simple equivalent changes and modifications made in accordance with the scope of the present invention's patent application and the description of the invention, is within the scope of the present invention.

(1): Test section (11): Positive displacement pump (12): Preheating water tank (13): Flow meter (14): Pressure regulating valve (15): Cooling water tank (16): Signal line (17): Data recorder (18): computer (19): thermocouple (2): single-well geothermal system (2A): dual-well geothermal system (21): cold working fluid (22): hot working fluid (3): pore Thermal model (31): inlet (32): outlet (4): cylindrical tube (41): porous medium

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

[Second figure] is a configuration diagram of a geothermal reservoir experimental system according to an 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 heating model according to an embodiment of the present invention.

[Fifth figure] is a comparison diagram of the temperature difference between the inlet and the outlet of the hollow tube at 10 and 30 MPa and the particle size of 1.54 mm and 2.03 mm with the flow rate according to the embodiment of the invention.

[Sixth figure] is a comparison diagram of the temperature difference between the heating section and the simulation result at the entrance and exit of the embodiment of the present invention.

[Seventh figure] is a comparison chart of the heat extraction results of the heat extraction result and the simulation result of the embodiment of the present invention.

[Figure 8] is a comparison diagram of longitudinal and lateral heat dissipation results of crack flow and pore flow at different flow rates according to an embodiment of the present invention.

[The ninth figure] is a schematic diagram of a single-well heat extraction model according to an embodiment of the present invention.

Claims (9)

A geothermal well productivity forecasting method includes: establishing a geothermal reservoir experiment system to obtain a heat extraction result through a heat extraction experiment. The geothermal reservoir experiment system is formed by a high-pressure test pipeline In the test section, the heat extraction result of a working fluid flowing through a reservoir is simulated; the heat extraction result is used to fit a pore heat extraction model to complete a simulated physical characteristic under pore and fracture conditions to establish a geothermal extraction The thermal model is based on the heat extraction result of the geothermal reservoir experimental system, and then using the computer to simulate the heat extraction phenomenon of the working fluid in the reservoir in a proportional manner according to the test section, 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 with a tube flow model simulation to establish a single well heat extraction model, input a geothermal production capacity at different time points Data, establish the total thermal resistance of a geothermal well, and compare the differences, so as to modify the single well heat extraction model, and use the geothermal reservoir experimental system in the computer to enlarge the current model with the pipe flow model to the local scale model. To form the single-well heat extraction model for practical application to the local geothermal system for evaluation; use an optimization method to make the single-well heat extraction model calculate the optimal heat extraction, and use the optimization method to calculate the Knowing the depth of the geothermal well and the pore state of the rock formation, and the most suitable geothermal well injection flow rate, to obtain the maximum amount of heat that can be extracted after flowing through the geothermal well. The geothermal well productivity estimation method as described in item 1 of the patent application scope, wherein the heat extraction result is included in the experimental physics of the working fluid under different medium particle sizes, pressures, flow rates, porosities, laminar flow states or flow rates Changes in characteristics. The method for estimating the productivity of a geothermal well as described in item 2 of the patent application scope, wherein the experimental physical characteristics include one of heat exchange efficiency, heat convection coefficient, heat dispersibility, heat extraction, or any combination thereof. The method for estimating geothermal well productivity as described in item 1 of the patent application scope, wherein the simulated physical properties include thermal dispersibility, thermal diffusivity, effective thermal conductivity, effective specific heat, longitudinal thermal dispersibility, and lateral thermal dispersibility One or any combination thereof. The method for estimating the productivity of a geothermal well as described in item 1 of the scope of the patent application, in which the pore heat extraction model in the computer is provided with the heat extraction result to fit the heat dispersibility under various pressure and flow conditions, And compare the difference between the heat extraction measured during the heat extraction experiment and the temperature difference between the inlet and outlet. The method for estimating geothermal well productivity as described in item 1 of the scope of the patent application, wherein the geothermal productivity data of any known geothermal well at the time is input into the single-well heating model to obtain the known geothermal well An old total thermal resistance, and then input the later geothermal productivity data into the single-well heating model to obtain a new total thermal resistance of the known geothermal well, and compare the difference between the new and old total thermal resistance, Therefore, 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 status of the known geothermal well with time. The method for estimating geothermal well productivity as described in item 6 of the patent application scope, 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. The method for estimating the productivity of a geothermal well as described in item 1 of the scope of the patent application, in which the pore heating model based on a Brinkman model and a heat transfer module is fitted by the computer as the basis The geothermal heat extraction model and the optimization method are simple conjugate gradient methods. A geothermal well productivity forecasting system is established by the geothermal well productivity forecasting method described in any one of items 1 to 8 of the patent application.

Images