Design Method and System for DTS Logging Acquisition Scheme in Oil Wells Based on Forward Model

Abstract

This invention belongs to the field of petroleum development technology and discloses a design method and system for DTS logging acquisition schemes in oil wells based on a forward model. The method includes: S1: conducting a production capacity test on the target well to determine the maximum permissible daily production; S2: establishing a forward model of the wellbore temperature based on the actual parameters of the target well and the determined maximum daily production; S3: changing the daily production and establishing a series of temperature measurement forward models to determine the optimal production operating regime for DTS logging of the target well; S4: after changing the operating regime, simulating reservoir temperature changes to determine the minimum time required to reach steady state under the optimal production operating regime, thereby determining the minimum monitoring time for DTS logging; S5: simulating temperature changes after well shut-in to determine the sampling time interval for the maximum temperature curve of the logging; S6: determining the DTS logging acquisition scheme based on the optimal production operating regime, the minimum monitoring time, and the sampling time interval. This method solves the existing problems encountered in the logging process.

Description

Technical Field

[0001] This invention belongs to the field of petroleum development technology, specifically relating to a design method and system for DTS logging acquisition schemes in oil wells based on forward modeling. Background Technology

[0002] Currently, distributed fiber optic DTS logging in major oilfields in China is still in the experimental and promotion stage. The lack of systematic pre-logging design before data acquisition has led to problems such as excessive useless duplicate data, large data anomalies, low signal-to-noise ratio, and low overall data utilization, hindering the widespread adoption of distributed fiber optic DTS logging technology. The cost of single-well DTS acquisition using distributed fiber optic logging is 5-10 times that of conventional production logging, and the application effect after testing fiber optics in some wells is not significant, even lagging behind conventional production logging, thus hindering the promotion of fiber optic logging. Furthermore, fiber optic logging cannot be used in the existing technology in the following situations: (1) Wells with severe water flooding and no remaining potential, where existing data can clearly solve the existing production problems, conventional production logging can solve the problems, production is normal, and there are no development problems; (2) 30% of the data acquisition interval is <2min, and the temperature data of a well within 2 days reaches thousands of channels, occupying more than 100M of space, resulting in a large amount of data duplication, slow data opening speed, and inability of interpretation software to load. Before processing, manual data thinning is required, which is a heavy workload. (3) In some oil wells, the DTS signal is flooded by noise signals due to the low logging flow rate, and the acquired data cannot be used for accurate flow rate splitting; distributed fiber optic DTS needs to monitor multiple operating regimes, and the temperature curve of some wells is not sensitive to the change of the flow rate value of the logging design operating regime, which reduces the overall accuracy of data interpretation. (4) After the adjustment of the fiber optic logging operation system, the monitoring time of some data is too short. The temperature field of the wellbore has not reached a stable state before the next operation system is started to measure. As a result, the measurement data cannot truly reflect the flow rate of the wellbore stratification. The flow rate interpreted by the temperature curve is inconsistent with the actual flow rate at the wellhead. This further leads to problems such as too much useless and repetitive data, large data anomalies, low signal-to-noise ratio, and low overall data utilization rate during logging. Summary of the Invention

[0003] The purpose of this invention is to provide a design method and system for DTS logging acquisition schemes in oil wells based on forward modeling, so as to solve the problems in the prior art.

[0004] To achieve the above objectives, the present invention adopts the following technical solution:

[0005] A method for designing DTS logging acquisition schemes for oil wells based on forward modeling includes:

[0006] S1: Conduct a production capacity test on the target well to determine the maximum permissible daily production;

[0007] S2: Based on the actual parameters of the target well and the determined maximum daily production, establish a forward model of the temperature inside the wellbore of the target well;

[0008] S3: Change the daily production rate, establish a series of temperature measurement forward models, and determine the optimal production operating system for the target well during DTS logging;

[0009] S4: After the working system is changed, the minimum time required to reach steady state under the optimal production working system is determined by simulating the reservoir temperature change, thereby determining the minimum monitoring time of DTS logging after the change of the oil well working system;

[0010] S5: By simulating the temperature change after the well is shut in, the sampling time interval of the maximum temperature curve of DTS logging under the optimal working conditions is obtained;

[0011] S6: Determine the DTS logging acquisition scheme for oil wells based on the optimal production operating system during DTS logging of the target well, the minimum monitoring time of DTS logging, and the time interval between sampling of the maximum temperature curve.

[0012] Furthermore, the time interval for sampling the temperature curve in S5 is the maximum sampling time interval. The maximum sampling time interval is calculated as follows: based on the temperature change curve of the well after shut-in in the forward modeling simulation, the maximum required sampling time interval is determined within the maximum allowable temperature difference between adjacent times in the initial stage of shut-in.

[0013] Furthermore, in S4, the forward model reaches a steady state when the rate of change of the temperature curve over time in the forward model approaches 0 after the production system is changed.

[0014] Furthermore, the optimal production operating regime for the target well in S3 is the region in the temperature and gas production data table where the temperature changes by no less than 1 with respect to gas production.

[0015] Furthermore, the actual parameters of the target well in S2 include the reservoir, wellbore size, and production data of the target well.

[0016] Furthermore, the target well is obtained by measuring actual wellhead data during the production capacity test.

[0017] Furthermore, the forward model for the target well is as follows:

[0018]

[0019] Where: Z is the measurement depth, T is the well temperature, and T0 is the well temperature. R P is the formation temperature, P is the wellbore pressure, P Rθ is the formation pressure, g is the gravitational acceleration, θ is the well inclination, U is the thermal conductivity of the wellbore and reservoir, i is the fluid phase, and Q is the relative density of each phase. i For phase flow, Q pi For the production of each phase, This represents the amount of gas released from the fluid. ρ represents the amount of volatile gas emitted from the reservoir. i For the density of each phase, C Pi For the heat capacity of each phase, η i For each phase, the Joule-Thomson coefficients are... For heat of vaporization, v i Let be the velocity of each phase.

[0020] Furthermore, the forward model of the target well is calculated using the conservation of total energy within the wellbore. The conditions for calculating the equation for the conservation of total energy within the wellbore are as follows: the temperature model within the wellbore is established under constant enthalpy conditions. When the enthalpy change ΔH = 0, the entire downhole system is in a constant pressure and adiabatic state. When the fluid within the wellbore is in stable flow, it enters the wellbore from the formation or enters the formation from the wellbore. The change in internal energy within the wellbore is equal to the work done on the system by the external environment.

[0021] Furthermore, when there is a temperature difference within the wellbore, the method for calculating the overall conductivity between the wellbore and the formation is as follows:

[0022]

[0023]

[0024] Among them, K Rock For the thermal resistance of rocks, K T For the thermal resistance of the oil pipe, K A For the annular thermal resistance, K C K is the thermal resistance of the bushing. cement For cement thermal resistance, OD T For the outer diameter of the oil pipe, ID T ID is the inner diameter of the oil pipe. C OD is the inner diameter of the casing. A For the outer diameter of the annulus, ID A OD is the inner diameter of the annulus. C D is the outer diameter of the casing. Rock Let T be the thermal diffusivity of the rock, and T be the time.

[0025] A forward model-based DTS logging acquisition system for oil wells includes:

[0026] The testing module is used to perform production capacity testing on the target well and determine the maximum permissible daily production.

[0027] The forward model building module is used to build a forward model of the temperature inside the wellbore of the target well based on the actual parameters of the target well and the determined maximum daily production.

[0028] The optimal production regime determination module is used to establish a series of temperature measurement forward models after changing the daily output to determine the optimal production regime for DTS logging of the target well.

[0029] The steady-state minimum time determination module is used to determine the minimum time required to reach steady state under the optimal production operating system after the change of the operating system, through reservoir temperature change simulation, thereby determining the minimum monitoring time of DTS logging after the change of the oil well operating system;

[0030] The sampling time interval determination module is used to obtain the sampling time interval of the maximum temperature curve of DTS logging under the optimal working conditions by simulating the temperature change after the well is shut in.

[0031] The hybrid module is used to determine the DTS logging acquisition scheme for oil wells based on the forward model, according to the optimal production operating conditions during DTS logging of the target well, the minimum monitoring time of DTS logging, and the time interval between sampling of the maximum temperature curve.

[0032] Compared with the prior art, the advantages of the present invention are:

[0033] By establishing a forward model of temperature within the wellbore and simulating the model, the relationship between temperature measurement curves and sampling intervals, operating procedures, and production time can be studied. This reduces duplicate data acquisition, makes the set operating procedures more reasonable, and makes the measured temperature curves more sensitive to flow rate changes. It avoids useless data acquisition caused by monitoring at excessively low flow rates. After changing the measurement operating procedures, it is possible to achieve full-process monitoring after the temperature reaches a stable state. This solves the problems that DTS data collected during well logging generally have too much useless duplicate data, large data anomalies, low signal-to-noise ratio, and low overall data utilization. Attached Figure Description

[0034] The accompanying drawings, which form part of this specification, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings:

[0035] Figure 1 This is a flowchart illustrating a design method for DTS logging acquisition schemes in oil wells based on a forward model, according to the present invention.

[0036] Figure 2 This is a schematic diagram illustrating the influence of flow rate variation on temperature curves in a design method for DTS logging acquisition schemes of oil wells based on a forward model, according to the present invention.

[0037] Figure 3 This is a schematic diagram illustrating the influence of flow rate variation on temperature curves in a design method for DTS logging acquisition schemes of oil wells based on a forward model, according to the present invention.

[0038] Figure 4 This is a schematic diagram showing the intersection of temperature and gas production in a DTS logging acquisition scheme design method for oil wells based on a forward model, according to the present invention.

[0039] Figure 5 This invention relates to a design method for DTS logging acquisition schemes in oil wells based on a forward model. The diagram shows the temperature measurement curve changes after the well is shut in. Detailed Implementation

[0040] The present invention will now be described in detail with reference to the accompanying drawings and embodiments. It should be noted that, unless otherwise specified, the embodiments and features described herein can be combined with each other.

[0041] The following detailed description is exemplary and intended to provide further detailed explanation of the invention. Unless otherwise specified, all technical terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art. The terminology used in this invention is for describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention.

[0042] This invention discloses a DTS logging acquisition method for oil wells based on a forward model. Employing distributed fiber optic DTS logging, it offers advantages such as high-temperature resistance, distributed measurement, and long-term monitoring. This invention addresses specific production problems encountered in oilfield development, such as low water production with low energy consumption, uneven efficiency, injection-production mismatch, and sudden water flooding, by using fiber optic logging technology and considering these issues. It identifies target wells for monitoring and solves testing and production problems that conventional production logging cannot address, fully leveraging the advantages of fiber optic logging technology. A basic forward model of the temperature field is established using formation, wellbore, and production parameters corresponding to the selected test well. Within the allowable production range, a set of adjustment values ​​is set for different production parameters. Multiple forward models are established by adjusting the target well's operating regime, injection-production volume, production time, and other specific production conditions. The influence of changes in measurement time intervals, operating regimes, and measurement waiting times under each operating regime on the measured temperature curve is studied. Ultimately, a distributed fiber optic DTS logging data acquisition scheme design method is formed, enabling accurate temperature-flow inversion. This method saves costs while improving data utilization efficiency.

[0043] This invention provides a distributed fiber optic DTS logging acquisition method for oil wells based on a forward model, such as... Figure 1 As shown.

[0044] A method for designing DTS logging acquisition schemes for oil wells based on forward modeling includes:

[0045] S1: Conduct a production capacity test on the target well to determine the maximum permissible daily production;

[0046] S2: Based on the actual parameters of the target well and the determined maximum daily production, establish a forward model of the temperature inside the wellbore of the target well;

[0047] S3: Change the daily production rate, establish a series of temperature measurement forward models, and determine the optimal production operating system for the target well during DTS logging;

[0048] S4: After the working system is changed, the minimum time required to reach steady state under the optimal production working system is determined by simulating the reservoir temperature change, thereby determining the minimum monitoring time of DTS logging after the change of the oil well working system;

[0049] S5: By simulating the temperature change after the well is shut in, the sampling time interval of the maximum temperature curve of DTS logging under the optimal working conditions is obtained;

[0050] S6: Determine the DTS logging acquisition scheme for oil wells based on the optimal production operating system during DTS logging of the target well, the minimum monitoring time of DTS logging, and the time interval between sampling of the maximum temperature curve.

[0051] Specifically, S1: Target well productivity test to determine the maximum permissible daily production.

[0052] Actual production tests were conducted on the target oil well to determine the maximum production rate and corresponding oil pressure that could be achieved in a stable production state. Then, the production rate was gradually reduced, and the corresponding wellhead oil pressure was recorded under the stable production state.

[0053] S2: Based on the actual parameters of the target well, establish a forward model of the temperature inside the wellbore.

[0054] Data related to the target well were collected, and a forward model was established using the forward modeling module of PLATO software based on parameters such as reservoir, wellbore, thermal parameters, PVT, and production.

[0055] S3: By changing the daily output, establish a series of temperature measurement forward models to determine the optimal production operating system for the target well.

[0056] Specifically, taking the maximum allowable oil production as an example, the maximum allowable oil production is divided into 10 equal parts at certain intervals. The temperature measurement curve is then simulated in forward modeling under the condition that the production is increasing from small to large. The first curve with a significant change between the temperature measurement and the ground temperature is selected as the minimum working state to be used during logging. According to the temperature curve interpolation, the maximum temperature and the first temperature curve are approximately divided into n parts. The corresponding flow rate values ​​are used as the intermediate n-2 measurement working states, where n represents the working state of the test, and n is preferably less than 5.

[0057] S4: After the working system is changed, the minimum time required to reach steady state after the change of each production system is determined by simulating the change of reservoir temperature.

[0058] Furthermore, the working procedures before and after the change were set up, and the temperature measurement curve was simulated over time after the change in working procedures. When the rate of change of the temperature curve over time approached 0, it was determined that the wellbore production after the change in working procedures had reached a stable state, and the corresponding time was recorded. (See attached diagram) Figure 3 The figure shows a simulation of temperature changes before the temperature curve reaches the warm state after the production is reduced. After the adjustment of this system, the minimum monitoring time of DTS is 5 hours, and usually it is necessary to monitor for 3-5 hours more to facilitate the selection of the best data during interpretation.

[0059] S5: Determine the sampling time interval of the temperature curve through forward modeling of temperature measurement.

[0060] The rate of temperature change over time is greatest during well shut-in. Forward modeling was used to simulate the temperature change over time after well shut-in. Based on the maximum allowable temperature difference between adjacent time points during the initial shut-in period, the required maximum sampling interval was calculated.

[0061] Specifically, the series of temperature measurement forward simulations involves changing the operating parameters multiple times and establishing multiple forward simulation models for comparison.

[0062] Furthermore, the parameters for the maximum daily production of the target well in S2 include the reservoir of the target well, the wellbore size, and the actual production data of the target well.

[0063] Furthermore, the optimal operating regime for the target well in S3 is the operating regime in which changes in the temperature curve are most sensitive to changes in flow rate.

[0064] Furthermore, in S4, the forward model reaches a steady state when the rate of change of the temperature curve over time in the forward model approaches 0 after the production regime is changed.

[0065] Furthermore, in S5, the temperature change curve over time after well shut-in is simulated using forward modeling. Based on the maximum allowable temperature difference between adjacent times during the initial shut-in period, the required maximum sampling interval is calculated.

[0066] Furthermore, the target well's productivity is obtained through actual wellhead data during production testing.

[0067] More specifically, the maximum sampling time interval is calculated as follows: based on the temperature change curve of the well after shut-in in the forward modeling, the maximum required sampling time interval is determined within the maximum allowable temperature difference between adjacent times in the initial stage of shut-in.

[0068] More specifically, the optimal production operating regime for the target well is defined as the region in the temperature and gas production data table where the temperature variation with gas production is not less than 1. For example... Figure 3As shown, the sensitive region is the rectangular area in the temperature-gas production rate versus time graph. Within this rectangular area, the changes in both temperature and gas production rate are greater than or equal to 1. Outside the sensitive region, the temperature-gas production rate changes with gas production rate are all less than 1. Within this sensitive region, the temperature-gas production rate changes significantly, better reflecting the relationship between temperature and gas production rate. Using this region as a reference, the optimal production regime can be determined.

[0069] More specifically, the forward model for the target well is as follows:

[0070]

[0071] Where: Z is the measurement depth, T is the well temperature, and T0 is the well temperature. R P is the formation temperature, P is the wellbore pressure, P R θ is the formation pressure, g is the gravitational acceleration, θ is the well inclination, U is the thermal conductivity of the wellbore and reservoir, i is the fluid phase, and Q is the relative density of each phase. i For phase flow, Q pi For the production of each phase, This represents the amount of gas released from the fluid. ρ represents the amount of volatile gas emitted from the reservoir. i For the density of each phase, C Pi For the heat capacity of each phase, η i For each phase, the Joule-Thomson coefficients are... For heat of vaporization, v i Let be the velocity of each phase.

[0072] Specifically, when establishing a forward model of temperature by collecting temperature data inside the target well, the total energy inside the well is conserved. The conditions for calculating the equation for the total energy conservation inside the well are as follows: the temperature model inside the well is established under constant enthalpy conditions. When the enthalpy change ΔH = 0, the entire downhole system is in a constant pressure and adiabatic state. When the fluid inside the well is in a stable flow, it enters the well from the formation or enters the formation from the well. The change in internal energy inside the well is equal to the work done on the system by the external environment.

[0073] Specifically, the general calculation formula between the wellbore and the formation is:

[0074]

[0075]

[0076] Among them, K Rock For the thermal resistance of rocks, K T For the thermal resistance of the oil pipe, K A For the annular thermal resistance, K C K is the thermal resistance of the bushing. cement For cement thermal resistance, OD T For the outer diameter of the oil pipe, IDT ID is the inner diameter of the oil pipe. C OD is the inner diameter of the casing. A For the outer diameter of the annulus, ID A OD is the inner diameter of the annulus. C D is the outer diameter of the casing. Rock Let T be the thermal diffusivity of the rock, and T be the time.

[0077] When performing the calculation, substitute equation (3) into equation (2), and finally substitute equation (2) into equation (1) to solve for the value of wellbore temperature T.

[0078] The invention has the following specific features: Based on parameters such as formation, wellbore, and production of the target well, it starts with forward modeling of the temperature field to study the sensitive relationship between temperature measurement curves and sampling intervals, operating regimes, and production time. Ultimately, it develops a pre-logging design scheme based on distributed fiber optic DTS logging forward modeling for the target well. Production experiments are conducted on the target well to determine the maximum permissible production rate without damaging the formation and achieving stable production. This production rate is used as the maximum oil production rate set in the forward modeling. Within the determined maximum production range, the production rate that is clearly distinguishable from the geothermal curve is selected as the minimum operating regime production rate. Based on the simulation results, several temperature curves that are more sensitive to flow rate are selected as several logging production operating regimes, such as... Figure 2 As shown; by using forward modeling to simulate the minimum time required to reach steady state after changes in the production work system, the monitoring time corresponding to each measurement work system is determined. Typically, the monitoring time is 3-5 hours longer than the minimum time. Figure 3 The figure shows the temperature change curve over time after well shut-in, simulated using forward modeling. Based on the maximum allowable temperature difference between adjacent times during the initial non-steady-state phase, the required maximum sampling interval is calculated, as shown below. Figure 4 As shown.

[0079] Furthermore, such as Figure 2 The diagram illustrates the influence of flow rate variation on temperature curves in a forward model-based DTS logging acquisition scheme design method for oil wells according to the present invention. Different flow rates correspond to different temperatures, also indicating the operating conditions. Figure 4-5 The figures show a schematic diagram of the intersection of temperature and gas production in a DTS logging acquisition scheme design method for oil wells based on a forward model, and a corresponding temperature curve change graph after well shut-in, respectively. The figures show that the time taken for the oil temperature to stabilize after the adjustment of the operating regime is shorter than the shut-in time, or the well shut-in time is longer than the time taken after the operating regime adjustment, further highlighting the superiority of the operating regime.

[0080] More specifically, the principle of the DTS logging acquisition method for oil wells based on a forward model of the present invention is as follows: (1) Determine the maximum allowable daily production through target well production capacity testing. Conduct actual production tests on the target oil well to determine the maximum production capacity and corresponding oil pressure that can reach a stable production state, and then gradually reduce the production capacity and record the corresponding wellhead oil pressure under the stable production state. (2) Establish a wellbore forward model using actual geological, wellbore, and production data of the target well. Collect relevant data of the target well, as shown in Appendix Table 1. Establish a forward model using the forward modeling module of PLATO software based on parameters such as reservoir, wellbore, thermal parameters, PVT, and production. (3) Establish a series of temperature measurement forward models by changing the daily production capacity to determine the optimal production operating system of the target well. The oil production was divided into 10 equal parts at certain intervals. The temperature measurement curve was simulated in forward modeling under the condition that the production was from small to large. The first curve with obvious changes in temperature and ground temperature was selected as the minimum working state selected for logging. According to the temperature curve interpolation, the maximum temperature and the first temperature curve were approximately divided into n parts. The corresponding flow rate value was used as the middle n-2 measurement working states, where n represents the working state of the test. n is generally selected to be less than 5. (4) After the working state was changed, the minimum time required to reach steady state after the change of each production system was determined by simulating the change of reservoir temperature. The working states before and after the change were set, and the change law of temperature measurement curve with time after the change of working state was simulated. When the rate of change of temperature curve with time tends to 0, it is determined that the well production after the change of working state has reached a steady state, and the corresponding time is recorded. See attached. Figure 3 As shown, this is a simulation of the temperature change before the temperature reaches the optimal state after the production of a certain well is reduced. After the adjustment of this system, the minimum monitoring time of DTS is 5 hours, and it is generally necessary to monitor for 3-5 hours longer to facilitate the selection of the best data during interpretation. (5) Determine the sampling time interval of the temperature curve through forward modeling. The temperature change rate with time is the largest when the oil well is shut in. The temperature change curve with time after the oil well is shut in is simulated through forward modeling. The maximum required sampling interval is calculated based on the maximum allowable temperature difference between adjacent times in the early stage of the unsteady state.

[0081] A forward model-based DTS logging acquisition system for oil wells includes:

[0082] The testing module is used to perform production capacity testing on the target well and determine the maximum permissible daily production.

[0083] The forward model building module is used to build a forward model of the temperature inside the wellbore of the target well based on the actual parameters of the target well and the determined maximum daily production.

[0084] The optimal production regime determination module is used to establish a series of temperature measurement forward models after changing the daily output to determine the optimal production regime for DTS logging of the target well.

[0085] The steady-state minimum time determination module is used to determine the minimum time required to reach steady state under the optimal production operating system after the change of the operating system, through reservoir temperature change simulation, thereby determining the minimum monitoring time of DTS logging after the change of the oil well operating system;

[0086] The sampling time interval determination module is used to obtain the sampling time interval of the maximum temperature curve of DTS logging under the optimal working conditions by simulating the temperature change after the well is shut in.

[0087] The hybrid module is used to determine the DTS logging acquisition scheme for oil wells based on the forward model, according to the optimal production operating conditions during DTS logging of the target well, the minimum monitoring time of DTS logging, and the time interval between sampling of the maximum temperature curve.

[0088] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0089] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0090] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0091] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0092] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A forward model-based oil production well DTS logging acquisition scheme design method, characterized in that, include: S1: Conduct a production capacity test on the target well to determine the maximum permissible daily production; S2: Based on the actual parameters of the target well and the determined maximum daily production, establish a forward model of the temperature inside the wellbore of the target well; S3: Change the daily production rate, establish a series of temperature measurement forward models, and determine the optimal production operating system for the target well during DTS logging; S4: After the working system is changed, the minimum time required to reach steady state under the optimal production working system is determined by simulating the reservoir temperature change, thereby determining the minimum monitoring time of DTS logging after the change of the oil well working system; S5: By simulating the temperature change after the well is shut in, the sampling time interval of the maximum temperature curve of DTS logging under the optimal working conditions is obtained; S6: Determine the DTS logging acquisition scheme for oil wells based on the optimal production operating system during DTS logging of the target well, the minimum monitoring time of DTS logging, and the time interval between sampling of the maximum temperature curve; The forward model of the target well is: ， in: To measure depth, For well temperature, For the formation temperature, For wellbore pressure, For formation pressure, It is the acceleration due to gravity. It is a well-sloping well. The thermal conductivity of the wellbore and reservoir. For each phase fluid, For phase flow, For the production of each phase, This represents the amount of gas released from the fluid. This refers to the amount of gas volatilized from the reservoir. For the density of each phase, For the heat capacity of each phase, For each phase, the Joule-Thomson coefficients are... Heat of vaporization Let be the velocity of each phase.

2. The method according to claim 1, wherein, The time interval for sampling the temperature curve in S5 is the maximum sampling time interval. The maximum sampling time interval is calculated as follows: based on the temperature change curve of the well after shut-in in the forward modeling simulation, the maximum sampling time interval is determined within the maximum allowable temperature difference between adjacent times in the initial stage of shut-in.

3. The method of claim 1, wherein, In S4, the forward model reaches a steady state when the rate of change of the temperature curve over time in the forward model approaches 0 after the production regime is changed.

4. The method of claim 1, wherein, The optimal production operating regime for the target well in S3 is the region in the temperature and gas production data table where the temperature changes by no less than 1 with respect to gas production.

5. The method of claim 1, wherein, The actual parameters of the target well in S2 include the reservoir, wellbore size, and production data of the target well.

6. The method of claim 1, wherein, The target well was obtained by measuring actual wellhead data during the productivity test.

7. The method of claim 1, wherein the method further comprises: The forward model of the target well is derived from the calculation of the total energy conservation within the wellbore. The conditions for calculating the equation of total energy conservation within the wellbore are as follows: the temperature model within the wellbore is established under constant enthalpy conditions. When the enthalpy change ΔH = 0, the entire downhole system is in a constant pressure and adiabatic state. When the fluid within the wellbore is in stable flow, it enters the wellbore from the formation or enters the formation from the wellbore. The change in internal energy within the wellbore is equal to the work done on the system by the external environment.

8. The method of claim 1, wherein, The formula for calculating the overall thermal conductivity between the wellbore and the formation is: wherein, is the rock thermal resistance, is the tubing thermal resistance, is the annulus thermal resistance, is the casing thermal resistance number, is the cement thermal resistance, is the tubing outside diameter, is the tubing inside diameter, is the casing inside diameter, is the annulus outside diameter, is the annulus inside diameter, is the casing outside diameter, is the rock thermal diffusivity, is the time.

9. A forward model based oil production well DTS logging acquisition system, characterized in that, include: The testing module is used to perform production capacity testing on the target well and determine the maximum permissible daily production. The forward model building module is used to build a forward model of the temperature inside the wellbore of the target well based on the actual parameters of the target well and the determined maximum daily production. The optimal production regime determination module is used to establish a series of temperature measurement forward models after changing the daily output to determine the optimal production regime for DTS logging of the target well. The steady-state minimum time determination module is used to determine the minimum time required to reach steady state under the optimal production operating system after the change of the operating system, through reservoir temperature change simulation, thereby determining the minimum monitoring time of DTS logging after the change of the oil well operating system; The sampling time interval determination module is used to obtain the sampling time interval of the maximum temperature curve of DTS logging under the optimal working conditions by simulating the temperature change after the well is shut in. The mixed module is used for determining a DTS logging acquisition scheme of the oil production well based on a forward model according to an optimal production work system during DTS logging of the target well, a monitoring minimum time and a maximum temperature curve sampling time interval of the DTS logging; The forward model of the target well is: ， in: To measure depth, For well temperature, For the formation temperature, For wellbore pressure, For formation pressure, It is the acceleration due to gravity. It is a well-sloping well. The thermal conductivity of the wellbore and reservoir. For each phase fluid, For phase flow, For the production of each phase, This represents the amount of gas released from the fluid. This refers to the amount of gas volatilized from the reservoir. For the density of each phase, For the heat capacity of each phase, For each phase, the Joule-Thomson coefficients are... Heat of vaporization Let be the velocity of each phase.

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