CN112343576B - Technological method for monitoring oil-gas well yield by using optical fiber sensing means - Google Patents
Technological method for monitoring oil-gas well yield by using optical fiber sensing means Download PDFInfo
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- E—FIXED CONSTRUCTIONS
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Abstract
The invention discloses a process for monitoring oil and gas well yield by using an optical fiber sensing means, and particularly relates to the field of oil well yield detection equipment, wherein the process comprises the following steps: equipment placement, installation and pressure test: laying impermeable cloth on the ground, and then placing a continuous oil pipe operation vehicle so that the central shaft of the roller is opposite to the wellhead and is 20-30m away from the wellhead; and the pressure test device is matched with a fracturing truck and a ground pipeline to test the pressure of the ground pipeline, the pump pressure is gradually increased during the pressure test, and the pressure relief correction is realized when the leakage is found and immediately stopped. According to the invention, horizontal well multi-section clustering fracturing is adopted, namely, each stage of fracturing section comprises a plurality of cluster perforations, on the other hand, when the discharge capacity is limited, excessive clusters in the section can lead to strong competition of perforation liquid inlet and crack initiation and crack extension, and due to the existence of rock mechanics and ground stress heterogeneity, cracks always initiate and expand at the perforation clusters of weak links, so that the perforation holes of a high-stress area are difficult to open, thereby reducing the cluster efficiency and ensuring the accuracy of integral detection.
Description
Technical Field
The invention relates to the technical field of oil well yield detection equipment, in particular to a process method for monitoring oil and gas well yield by using an optical fiber sensing means.
Background
In recent years, with the development of data processing technology and the improvement of computing power of computers, big data technology gradually enters various industries, and helps people to break through in some technological front fields. However, in the oil and gas industry, there is often a lack of continuous high quality downhole and reservoir data, and it is difficult to reach a qualitative singularity through the accumulation of data volumes. In particular to the field of oil gas development, as resource development is continued to be in depth, the overall exploitation difficulty is increased day by day, and reservoir knowledge and development decisions have obvious uncertainty. This uncertainty is particularly evident in the development of unconventional resources, which often results in increased production costs or less than expected levels of overall reservoir development.
The DTS technique is based on temperature information carried by light raman scattering. When a laser pulse propagates in the optical fiber, energy level transitions are generated due to energy exchange between thermal vibrations of the fiber molecules and photon interactions, and raman scattering is generated. Raman scattering is an inelastic scattering, meaning that the energy of the scattered light is different from the energy of the incident light.
Whole well production profile analysis is one of the most important applications of optical fiber sensing technology in the oilfield industry. Its effectiveness has been demonstrated in a number of different formations and well conditions throughout the world. Conventional wellbore monitoring means, such as turbine production logging, are single-point flow monitoring means, and cannot provide continuous full wellbore information, which is an advantage of optical fiber sensing technology. To date, distributed fiber optic temperature sensing (DTS) in combination with data modeling techniques remains the most dominant fiber optic production profile monitoring approach. As mentioned above, the application of distributed fiber technology involves a multi-disciplinary comprehensive study, in which a single application of production profile monitoring, distributed fiber temperature sensing (DTS) provides a large number of continuous temperature data, and data analysts obtain information of well fluids through modeling.
Defects exist in all optical fibers, and in general, defects in optical fibers can cause a series of negative effects such as reduced transmission signals. However, for distributed optical fiber acoustic sensing (DAS), rayleigh light scattering due to fiber imperfections forms the basis for monitoring. It is worth mentioning that rayleigh light scattering is the signal of greatest intensity in scattered light.
The method comprises the steps of acquiring various parameters such as oil-water mixing density, gas-oil ratio, sucker rod weight, friction coefficient, indicator diagram parameters and the like in the prior art, and then carrying out complex operation according to a plurality of formulas to obtain the oil well yield. The prior art applies the above formula to well production calculations for all wells. However, the inventors herein have found that the errors in well production are different from well to well as determined by prior art formulas and that the errors in well production are larger for some wells as determined by prior art formulas.
Disclosure of Invention
In order to overcome the defects in the prior art, the invention provides a process method for monitoring the output of an oil-gas well by using an optical fiber sensing means, wherein a horizontal well multi-stage clustering fracturing is adopted, namely a plurality of cluster perforations are included in each stage of fracturing stage, so that the SRV is expected to be increased to improve the output, on the other hand, when the displacement is limited, excessive cluster numbers in the stage can cause the situation that the perforation liquid inlet and the crack start and crack extend to be competitive, and due to the existence of rock mechanics and ground stress heterogeneity, the crack always starts and expands at the perforation cluster of a weak link, so that the perforation of a high stress area is difficult to open, thereby reducing the cluster efficiency, and also ensuring the accuracy of integral detection, so as to solve the problems in the background art.
In order to achieve the above purpose, the present invention provides the following technical solutions: a process for monitoring oil and gas well production by means of optical fiber sensing, said process comprising:
step a, equipment placement, installation and pressure test; the method specifically comprises the following steps: laying impermeable cloth on the ground, and placing a coiled tubing operation vehicle so that the central shaft of the roller is opposite to the wellhead and is 20-30m away from the wellhead; the fracturing truck and the ground pipeline are connected by the fracturing unit, the ground pipeline pressure test is carried out, the pump pressure is gradually increased during the pressure test, the pump is immediately stopped for pressure relief and correction when leakage is found, the pressure test is 3.5MPa, the pressure is stabilized for 5min, the pressure is stabilized for 15min when no leakage is pressurized to 45MPa, and the pressure drop is smaller than 0.5 MPa.
Step b, testing tools; the method specifically comprises the following steps: hoisting the injection head and the blowout preventer to a designated position by using a crane, and connecting a power pipeline for functional test; operating all levels of flashboards of the BOP, observing that all levels of flashboards are normally opened and closed from the top end of the BOP and the indication pins, and corresponding to the operation actions; after the test is qualified, all flashboards of the BOP are positioned at the full-open position; connecting a coiled tubing guide using a special tool; inserting a coiled tubing into the injector head; connecting a continuous oil pipe connector and a test pulling/pressure testing disc, testing the tensile strength of a continuous oil pipe joint by pulling a filling head, wherein the joint part and the continuous oil pipe body do not slide, and the joint and the pin are not damaged; connecting a blowout preventer to a wellhead; the lubricator is connected to the lower part of the blowout preventer, and the lubricator is connected to the blowout preventer; a guy rope is tied, so that the wellhead device is ensured to have no shaking; closing the blowout prevention box, and pressing the blowout prevention box, the blowout prevention pipe and the blowout preventer through the coiled tubing to connect, seal and test pressure; closing the totally-enclosed flashboard, and performing pressure test on the totally-enclosed flashboard of the blowout preventer from fracturing eight-way pressing; opening the fully-sealed flashboard, enabling an oil pipe to descend below the blowout preventer half-sealed flashboard, closing the blowout preventer half-sealed flashboard, and performing pressure test on the blowout preventer half-sealed flashboard through continuous oil pipe pressing;
step c, data acquisition; the method specifically comprises the following steps: installing a well tool, and testing the performance of the tool; when the injector is in a well, the injector head directional control rod is slowly operated to lower the oil pipe; lifting the continuous oil pipe after the continuous oil pipe is lowered to a specified depth, so that the continuous oil pipe is in a stretching state; the injection head and the roller brake, the equipment maintains pressure, and the system pressure and the well casing pressure of the equipment are monitored; notifying an optical monitoring person to perform the next construction; after all data acquisition is completed, after the data acquisition is completed, starting to lift the continuous oil pipe and lifting out of the wellhead;
step d: analyzing data; the method specifically comprises the following steps: forward modeling: analyzing well structure, well completion information, stratum property, production data and well parameters by using Production Inverse software to establish various well bore fluid types and thermodynamic models, and selecting one well bore fluid type to obtain a ground temperature curve, namely a curve of temperature change along with the increase of well depth;
reverse modeling: by utilizing data software, the data of the DTS temperature is input into the software, the software simulates energy change caused by fluid flow below each point in a well bore and energy change caused by formation fluid output, and then the output of each cluster is simulated and calculated, and a plurality of DTS temperature-output change curves are obtained through simulation;
the reverse modeling analysis result is brought into forward modeling, if the data comparison result passes, the next step is carried out, if the result does not pass, the reverse modeling is repeated until the forward and reverse modeling data are deduced to be consistent;
and (3) data modeling eliminates multiple solutions, and simultaneously performs error analysis and quality control.
Preferably, the collecting step in the step c specifically comprises the following steps: (1) Closing the well, collecting a ground temperature baseline, and taking an average result of data in the closing period as the ground temperature baseline in data analysis;
(2) Opening a well, and carrying out production monitoring of a first production system: a certain amount of oil-water enters a shaft from a stratum to cause different temperature changes, and an optical fiber in a continuous oil pipe can continuously record a temperature process;
(3) Changing production monitoring of the second production system, and recording the change condition of the whole well temperature after the change of the bottom hole differential pressure;
(4) And closing the well, and measuring a ground temperature return baseline.
Preferably, the device in step c includes a DTS optical transceiver and an OTDR, the DTS optical transceiver is utilized to polish the optical fiber coiled tubing, the optical fiber is used as a sensing element and a transmission signal medium, and the OTDR is utilized to detect disturbance information of external signals distributed on the sensing optical fiber.
The beneficial effects of the invention are as follows: according to the invention, horizontal well multi-section clustering fracturing is adopted, namely, a plurality of cluster perforation holes are contained in each stage of fracturing section, so that the SRV is expected to be increased, and the yield is improved, on the other hand, when the displacement is limited, too many clusters in the section can lead to the situation that the perforation holes enter liquid and the crack is initiated to be cracked and extend to be competitive, and due to the existence of rock mechanics and ground stress heterogeneity, the crack always initiates to crack and extend at the perforation cluster of a weak link, so that the perforation holes of a high stress area are difficult to open, thereby reducing the cluster efficiency and also ensuring the accuracy of integral detection.
Drawings
FIG. 1 is a data processing flow diagram of the present invention;
FIG. 2 is a wellbore modeling model diagram;
FIG. 3 is an exemplary graph of pressure changes as a reservoir is produced;
FIG. 4 is a well ground temperature simulation plot;
FIG. 5 is a wellbore fluid modeling pattern under different completion modes;
FIG. 6 is a model of wellbore thermodynamic modeling under different completion modes;
FIG. 7 is a fluid thermodynamic model of a wellbore;
FIG. 8 is a first production regime temperature profile;
FIG. 9 is a second production regime temperature profile;
FIG. 10 is a third production regime temperature profile;
FIG. 11 is modeling results for a first operating regime for a XX well;
FIG. 12 is a modeling result for a first production regime for a simulated XX well;
FIG. 13 is a comparison of actual production versus production obtained with a large error model for the first production regime for XX wells;
FIG. 14 is modeling results for a second operating regime for a XX well;
FIG. 15 is a modeling result for a second production regime for a simulated XX well;
FIG. 16 is a comparison of actual production versus production obtained with a large error model for the second production regime for XX wells;
FIG. 17 is modeling results for a third operating regime for a XX well;
FIG. 18 is a modeling result for a third production regime for a simulated XX well;
FIG. 19 is a comparison of actual production versus production obtained from a large error model for a third production regime for XX wells;
FIG. 20 is a graph showing cumulative liquid production for each cluster under three production regimes;
FIG. 21 is an average production contribution from actual production versus ideal for each cluster of XX wells;
FIG. 22 is a comparison of fluid production for each cluster under three production regimes with integrated interpretation and fracturing scale;
FIG. 23 is a graph showing the contribution rate of oil production from each cluster under three production regimes;
FIG. 24 is a graph showing the contribution rate of water produced by each cluster under three production regimes;
FIG. 25 is a graph of the rate of fluid contribution for each segment under three production regimes;
FIG. 26 is a full well bore fine temperature profile of a XX well;
figure 27 is a plot of the propped dose and liquid volume per segment versus the liquid production rate for three production regimes.
Detailed Description
Further advantages and effects of the present invention will become apparent to those skilled in the art from the disclosure of the present invention, which is described by the following specific examples.
Please refer to the figure. It should be understood that the structures, proportions, sizes, etc. shown in the drawings are for illustration purposes only and should not be construed as limiting the invention to the extent that it can be practiced, since modifications, changes in the proportions, or otherwise, used in the practice of the invention, are not intended to be critical to the essential characteristics of the invention, but are otherwise, required to achieve the objective and effect taught by the invention. Also, the terms "upper", "lower", "left", "right", "middle" and "a" are used herein for descriptive purposes only and are not intended to limit the scope of the present invention, but rather the relative relationship changes or modifications within the context of the present invention should be construed to mean the scope of the present invention without substantial modification.
Referring to fig. 1 to 27, an embodiment of the present invention includes:
a process for monitoring oil and gas well production by optical fiber sensing means, comprising: step a, equipment placement, installation and pressure test; the method specifically comprises the following steps: laying anti-seepage cloth on the ground, placing a coiled tubing operation vehicle, enabling a central shaft of a roller to be opposite to a wellhead and 20-30m away from the wellhead, and paying attention to: (1) In the process of placing the vehicles, a special person is required to command the vehicles to be placed, so that the vehicles are prevented from scraping people and objects; (2) Placing the vehicle in place, and placing the wood masking to ensure that the underground pipeline is not pressed and the equipment is not sunk; (3) After the vehicle is placed, carrying out tour inspection on the vehicle according to the operation rules, so as to ensure that the vehicle can work normally; the construction area is pulled with warning lines, and warning signs, emergency escape routes and emergency gathering point signboards are placed;
the fracturing truck and the ground pipeline are connected by the fracturing unit, the ground pipeline pressure test is carried out, the pump pressure is gradually increased during the pressure test, the pump is immediately stopped for pressure relief and correction when leakage is found, the pressure test is 3.5MPa, the pressure is stabilized for 5min, the pressure is stabilized for 15min when no leakage is pressurized to 45MPa, and the pressure drop is smaller than 0.5 MPa.
Step b, testing tools; the method specifically comprises the following steps: hoisting the injection head and the blowout preventer to a designated position by using a crane, and connecting a power pipeline for functional test;
operating all levels of flashboards of the BOP, observing that all levels of flashboards are normally opened and closed from the top end of the BOP and the indication pins, and corresponding to the operation actions; after the test is qualified, all flashboards of the BOP are positioned at the full-open position;
connecting a coiled tubing guide using a special tool;
inserting a coiled tubing into the injector head;
connecting a continuous oil pipe connector and a test pulling/pressure testing disc, slowly increasing the pulling force of an injection head to 50KN, testing the tensile strength of a continuous oil pipe joint, ensuring that the joint part and a continuous oil pipe body do not slide, ensuring that the joint and a pin are not damaged, slowly increasing the pulling force of the injection head to 100KN after the joint and the pin are tested to be qualified, keeping for 2-3min, ensuring that no displacement exists, injecting water into the continuous oil pipe until the outlet of the continuous oil pipe is discharged, closing a pressure relief valve of the test pressing disc, and ensuring that the pressure drop is less than 0.5MPa for qualification, wherein the pressure drop is 5MPa, 15MPa and 35 MPa;
connecting a blowout preventer to a wellhead;
connecting a lubricator to the underside of the blowout preventer and connecting the lubricator to the blowout preventer;
the guy rope is well tied, so that the wellhead device is ensured to have no shaking, the wellhead 2# gate is ensured to be closed, and the 3# gate is ensured to be opened;
closing the blowout prevention box, pressing the blowout prevention box, the blowout prevention pipe and the blowout preventer through a continuous oil pipe, connecting and sealing the blowout prevention box, the blowout prevention pipe and the blowout preventer, testing the pressure for 15min, and releasing the pressure to 0MPa through a bypass after the pressure test is finished, wherein the pressure drop is smaller than 0.5 MPa;
closing the totally-enclosed flashboard, testing the pressure of the totally-enclosed flashboard of the blowout preventer from fracturing eight-way pressing, testing the pressure of 25MPa, stabilizing the pressure for 15min, and releasing the pressure to 0MPa through a bypass after the pressure test is finished, wherein the pressure drop is not more than 0.5 MPa;
opening the fully-sealed flashboard, placing an oil pipe below the blowout preventer half-sealed flashboard, closing the blowout preventer half-sealed flashboard, performing pressure test on the blowout preventer half-sealed flashboard by pressurizing a continuous oil pipe, performing pressure test on the blowout preventer half-sealed flashboard for 50MPa, stabilizing the pressure for 15min, and releasing pressure to 0MPa by a bypass after the pressure test is finished, wherein the pressure drop is not more than 0.5 MPa.
Step c, data acquisition; the method specifically comprises the following steps: installing a well tool, and testing the performance of the tool; when the well is run, the injection head directional control rod is slowly operated to run the oil pipe, the speed of the oil pipe is not more than 5m/min when the oil pipe passes through the blowout preventer and the fracturing wellhead, the running condition of equipment is observed by testing for 50m, and white paint marks are respectively made in front of the counter at the positions of 20m and 50m where the continuous oil pipe runs. After the speed is normal and is not more than 25m/min, carrying out lifting and lowering test once every 300m of lowering test, and recording the suspended weight;
lifting the coiled tubing for 3m after being lowered to a specified depth, so that the coiled tubing is in a stretched state; the injection head and the roller brake, the equipment maintains pressure, and the system pressure and the well casing pressure of the equipment are monitored;
notifying an optical monitoring person to perform the next construction;
after all data acquisition is completed, after the data acquisition is completed, starting to lift the continuous oil pipe and lifting out of the wellhead;
the step c of collecting specifically comprises the following steps:
(1) Closing the well, collecting a temperature baseline close to the ground temperature, and taking an average result of data in the closing period as the ground temperature baseline in data analysis;
(2) Opening a well, and carrying out production monitoring of a first production system: a certain amount of oil-water enters a shaft from a stratum to cause different temperature changes, and an optical fiber in a continuous oil pipe can continuously record a temperature process;
(3) Changing the second production system and the third production system to monitor production, and recording the change condition of the whole well temperature after the change of the bottom hole pressure difference;
(4) And closing the well, and measuring a ground temperature return baseline.
Step d: analyzing data; the method specifically comprises the following steps: forward modeling: analyzing well structure, well completion information, stratum property, production data and well parameters by using Production Inverse software to establish various well bore fluid types and thermodynamic models, and selecting one well bore fluid type to obtain a ground temperature curve, namely a curve of temperature change along with the increase of well depth;
reverse modeling: by utilizing data software, the data of the DTS temperature is input into the software, the software simulates energy change caused by fluid flow below each point in a well bore and energy change caused by formation fluid output, and then the output of each cluster is simulated and calculated, and a plurality of DTS temperature-output change curves are obtained through simulation;
the reverse modeling analysis result is brought into forward modeling, if the data comparison result passes, the next step is carried out, if the result does not pass, the reverse modeling is repeated until the forward and reverse modeling data are deduced to be consistent;
the data modeling excludes multiple solutions, performs error analysis and quality control at the same time, and the flow of all data processing can be briefly summarized as shown in fig. 1.
The specific modeling process comprises the following steps:
(1) Wellbore modeling from XX well data, wellbore modeling is first performed as shown in fig. 2.
(2) Inputting key parameters
Before Production Inverse software performs simulation iterative computation on the yield, key information of the well needs to be input, and the software can subsequently perform modeling simulation computation by adopting the information of the well. After entering the reservoir pressure, geologic information, production fluid information, well structure, and thermodynamic parameters of the present well, the software simulates the downhole formation pressure, as shown in FIG. 3, fluid flow rate, joule-Thomson effect, in preparation for subsequent calculation of the full wellbore mass, energy, and momentum balance.
(3) Ground temperature curve simulation
And simulating a ground temperature curve of the well according to the well deviation data and the well bottom temperature obtained by DTS monitoring, and using the ground temperature curve as reference data for subsequent software data processing, as shown in fig. 4.
(4) Wellbore fluid and thermal model selection
Wellbore fluids and thermodynamic modeling types for different well completion modes are shown in fig. 5 and 6. The wellbore fluid type and thermodynamic model needs to be selected in Production Inverse software before production profile analysis can be performed. In the XX well optical fiber oil and water production testing process, a coiled tubing with a single flow valve in the well is used for testing the temperature, the optical fiber is deployed in the coiled tubing, the flowing direction in a shaft is unidirectional, and the modeling mode of the shaft is mode II.
(5) Conservation of mass, energy and momentum
The temperature at a point in the wellbore is affected not only by the JT effect produced by the produced fluid at that point, but also by the temperature change produced by the produced fluid flowing past that point. The data modeling needs to perform finite element modeling according to actual wellbore conditions, stratum information and wellhead information, a fluid flow model and a thermodynamic model are established, and each point in the well in the simulation process needs to ensure conservation of mass, energy and momentum. In the modeling process of the conservation of mass, energy and momentum, software simulates energy changes caused by fluid flow below each point in a well bore and energy changes caused by formation fluid production, so that the production of each cluster is calculated in a simulated manner, and meanwhile, the interference of lower-layer produced fluid on the production data of the layer is eliminated.
The flow of fluid from a high pressure reservoir into a low pressure wellbore is an exothermic process that is manifested as an increase in temperature at that point as the downhole fluid is produced. Theoretically, the more liquid is produced at this point, the more the temperature at this point increases. Oil and water have different thermodynamic properties, and oil-water of different properties produces different amounts of heat under different pressure differentials. In the software analysis process, the computer firstly randomly assigns values to the bottommost point in the well, simulates the oil and water production condition of the point, enables the temperature change of the oil and water production under a specific pressure difference to be consistent with the temperature curve of the point obtained by actual monitoring, and brings the oil and water production condition of the point into the previous point for simulation calculation until the energy and mass conservation is achieved, so as to obtain the oil and water production condition of the point. And by analogy, finally obtaining the oil and water production and contribution rate of each cluster of the whole shaft, wherein a fluid thermodynamic model in the shaft is shown in figure 7.
(6) Selection and processing of data
In the production monitoring operation process, production data including wellhead pressure and yield at the ground are required to be monitored while optical fiber distributed temperature is monitored. In this operation, the surface production data of the present well under three production regimes are shown in table 1. And (3) taking the obtained ground production data into software, selecting and preprocessing the temperature data obtained by monitoring, and carrying out reverse modeling on the temperature data to obtain the quantitative yield of each cluster.
Table 1 XX well production data under three production regimes
| Production system | First production system | Second production system | Third production system |
| Wellhead pressure (MPa) | 1.00 | 0.80 | 0.60 |
| Liquid yield (m) 3 /d) | 22.60 | 30.00 | 69.60 |
| Mass water content (%) | 65.50 | 62.90 | 41.20 |
| Volume moisture content (%) | 63.80 | 61.10 | 40.80 |
| Oil production (m) 3 Day/sky | 8.20 | 11.70 | 41.20 |
| Yield (m) 3 Day/sky | 14.40 | 18.30 | 28.40 |
After key parameters of the well are input in software, the monitored DTS temperature data are required to be selected and processed, and the temperature data when the well reaches an equilibrium state are required to be selected for the next simulation calculation. The data selection for the different phases of the XX well is based on the following.
1. Ground temperature baseline
The first shut-in starts at day 16:00 of month 3 of 2019 and ends at day 07:00 of month 15, and the monitoring time period is 15 hours. And combining the change trend of the underground temperature and the wellhead pressure, wherein the formation temperature reaches a relatively stable state within the period of 17:56-18:56 of 15 days of 3 months, and selecting the data averaging result in the interval as a ground temperature baseline in data analysis.
According to the observation of the ground temperature baseline in the well closing stage of the well, the well closing transient analysis is carried out, the XX well whole well shaft Wen Jixian has no abnormal point, the well shaft of the well has good integrity, and the well shaft has no flow point.
2. Stable temperature profile selection under first production regime
First production regime production fluid monitoring was from 3 months, 16 days, 20:30, 3 months and 17 days are finished, and the monitoring time is 13.5 hours. And combining the downhole temperature, wellhead pressure and yield change trend, wherein the production reaches a relatively stable state in the period of 8:26-9:26 on the day of 3 months 17, and taking the average result of the interval data as a production curve under the first production system.
The temperature curve and the ground temperature baseline under the first production system are shown in fig. 8, and the simulated temperature curve is obtained by carrying out software reverse stack modeling to simulate the yield distribution.
3. Stable temperature profile selection under a second production regime
The liquid production monitoring of the second production system is started from 13:50 on the 17 th day of 3 months, and is finished from 21:00 on the 17 th day of 3 months, and the monitoring time period is 7 hours. And combining the change trend of the downhole temperature, the wellhead pressure and the yield, wherein the production reaches a relatively stable state in the period of 16:19-17:19 on the day of 17 months, and taking the average result of the interval data as a stable production temperature curve under the second production system.
The second production system temperature curve and the ground temperature baseline are shown in fig. 9, and are brought into software reverse stack modeling to obtain a simulated temperature curve and simulate the yield distribution.
4. Stable temperature profile selection under third production regime
The third production regime production monitoring starts at 09:50 on day 3 month 18 and ends at 22:00 on day 3 month 18, with a monitoring period of 12 hours. And combining the change trend of the downhole temperature, the wellhead pressure and the yield, wherein the production reaches a relatively stable state in the period of 20:59-21:59 for 3 months and 18 days, and taking the average result of the interval data as a stable production temperature curve under a third production system.
The third production system temperature curve and the ground temperature baseline are shown in fig. 10, and are brought into software reverse stack modeling to obtain a simulated temperature curve and simulate the yield distribution.
(7) Data analysis
From the overall process interpretation flow, it can be seen that the accuracy of the model is largely dependent on the stability of the fluid state. The model established by the well is the first case and is very accurate. And quantitatively analyzing the data by using a mathematical modeling analysis mode, wherein the average single point value error of the modeling result and the actual measurement result is less than 5%.
Substituting the ground production data and the DTS temperature data into analysis software, establishing production models under each production system to obtain reverse modeling simulation curves, and continuously carrying out iterative calculation to compare and correct positive and negative model results. FIG. 11 is a simulation result of a certain time modeled under the first working regimen in the quantitative analysis process of the present well. As can be seen from the curve fitting result obtained by the simulation on the right side in the figure, a large difference exists between the temperature curve obtained by the reverse modeling simulation (the fourth line from the left) and the temperature curve obtained by the actual DTS monitoring (the third line from the left), a large error exists in the simulation result (the first line from the left), the quality control requirement is not met, the simulation parameters should be further optimized, and the error is reduced to obtain the optimal solution as shown in FIG. 12. And obtaining a final accurate and reliable modeling result through continuous simulation optimization, wherein the result meets the quality control requirement, and the average single-point error percentage of mathematical modeling and actual detection results is compared with 0.03%. In which the pair of production profile and actual production profile data corresponding to the larger model error is as shown in fig. 13, it can be seen that different yields correspond to different models, and only the error conforming to the actual yield model is minimum, and the yield profile at this time is the actual production profile.
FIG. 14 is a simulation result of a certain time modeled under the second working regime in the present well quantitative analysis process. As can be seen from the curve fitting result obtained by the simulation on the right side in the figure, a large difference exists between the temperature curve obtained by the reverse modeling simulation (the fourth line from the left) and the temperature curve obtained by the actual DTS monitoring (the third line from the left), a large error exists in the simulation result (the first line from the left), the quality control requirement is not met, the simulation parameters should be further optimized, and the error is reduced to obtain the optimal solution as shown in FIG. 15. And obtaining a final accurate and reliable modeling result through continuous simulation optimization, wherein the result meets the quality control requirement, and the average single-point error percentage of mathematical modeling and actual detection results is compared with 0.033%. The pair of production profile and actual production profile data corresponding to the larger model error is shown in fig. 16.
FIG. 17 is a simulation result of a certain time modeled under a third working regime in the present well quantitative analysis process. As can be seen from the curve fitting result obtained by the simulation on the right side in the figure, a large difference exists between the temperature curve obtained by the reverse modeling simulation (the fourth line from the left) and the temperature curve obtained by the actual DTS monitoring (the third line from the left), a large error exists in the simulation result (the first line from the left), the quality control requirement is not met, the simulation parameters should be further optimized, and the error is reduced to obtain the optimal solution as shown in fig. 18. And obtaining a final accurate and reliable modeling result through continuous simulation optimization, wherein the result meets the quality control requirement, and the mathematical modeling and the actual detection result are compared with each other to obtain an average single-point error percentage of 0.043%. The data pair of the production profile corresponding to the larger model error and the actual production profile is shown in fig. 19, and the average single-point error percentage of the mathematical modeling and the actual detection result is shown in table 2.
TABLE 2
(8) Qualitative analysis
Qualitative analysis is the analysis of data by fiber engineers in the field without modeling and quantitative calculations. Qualitative data analysis can obtain a large amount of information, and a data analyst with abundant experience can read the data and even obtain a lot of information which cannot be obtained by modeling. In the process of optical fiber production monitoring, a field optical fiber engineer can utilize fiber view software to observe the underground temperature condition in real time, and timely perform qualitative judgment on the underground abnormal condition and production condition according to judgment of temperature information. The Joule-Thomson effect occurs when fluid is produced downhole, the process of fluid production being an exothermic process, the temperature being manifested as an increase. Based on this feature, the field engineer will qualitatively make an analytical decision.
The optical fiber engineer completes the qualitative analysis of the test result of the well in the XX well optical fiber oil and water production profile test operation site, and the qualitative analysis of the well is mainly divided into two parts.
1. Shut-in analysis
By monitoring the temperature profile during the shut-in of the present well, 14 days, 3 months, 16: and each layer gradually recovers to the temperature of the stratum during 7:00 well closing in 3 months and 15 days of 00-2019, the stable state is achieved after 15 hours, heat exchange balance between liquid in the well bore and the stratum is achieved, and the temperature information of the stratum is effectively reflected. The observation of the temperature change trend of each horizon during the test shows that no obvious temperature change caused by JT effect is found according to the temperature curve of the fiber View software. No abnormal temperature point is found on the Guan Jingqu line of the well, and a light engineer qualitatively judges that the whole well of the well is good in the well in the field, and no flowing point exists in the well.
2. Production analysis
As downhole fluids are produced, fluid flow from the high pressure reservoir into the low pressure wellbore is an exothermic process due to the joule-thomson effect of the fluid in the wellbore, which is manifested as an increase in temperature at that point. Theoretically, the more liquid is produced at this point, the more the temperature at this point increases.
Qualitative observation of the comparison of the local well ground temperature base line and the three production curves during the test shows that in the area near the horizontal section B target point (horizontal section toe end), the gap between the production temperature curves and the ground temperature base line under the three production systems is not obvious, which indicates that the liquid production amount is possibly not ideal, so that the temperature change caused by the large Joule-Thomson effect is not generated on the temperature performance. Meanwhile, according to the temperature position relation between the three production curves and the ground temperature base line, the temperature of the production temperature curve under the third production system is relatively highest, and the liquid yield is judged to be the largest under the third production system in the three production systems qualitatively.
(9) Quantitative production analysis
Based on the reverse modeling simulation results, XX well clustering and segmentation yields are obtained as shown in tables 1 and 2.
Table 1 XX well cluster production monitoring results
Table 2 XX well section production monitoring results
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1. Production conditions of each cluster and perforation cluster effectiveness evaluation under three production systems
The production conditions of each cluster under three production systems were comprehensively analyzed, and the accumulated liquid production contribution rate of each cluster under three production systems is shown in fig. 20. It can be observed by comparison that cluster 3, 4, 5 and 35 had no liquid output under all three production regimes. If a perforation cluster contributing to yield under any production system is defined as an effective perforation cluster, the effectiveness of the whole perforation cluster of the well reaches 94%, which indicates that most cracks still start under the conditions of reducing the cluster spacing and increasing the crack density.
Ideally, the perforation clusters within each segment should reach an average extension state and the cluster yields should be relatively average, regardless of the inter-segment yield differences. If there are three perforation clusters in a segment, the yield contribution of each cluster should account for 33% of the yield of the segment; if there are two clusters in a segment, then the yield per cluster should ideally be 50% of the yield of the segment. FIG. 21 is a graph of actual production of clusters from the XX well as a percentage of total production of the present segment versus the average contribution of clusters in the ideal state. By comparison, although 94% of perforation clusters of the well have liquid production contribution and achieve the effect of cracking, the yield difference among clusters is large, and the extension condition of cracks of each cluster is greatly different. Meanwhile, the perforation clusters with the liquid production percentage of each cluster of the well accounting for more than 25% of the difference between the liquid production percentage of each section and the average contribution ratio are counted, and the statistical results are shown in the table 3, so that the difference between the yield contribution of about 50% of the perforation clusters and the average contribution ratio under an ideal state is larger, and the fact that the extension difference of cracks of each cluster of the well is larger is shown. From the viewpoint of crack extension uniformity, cluster spacing reduction is probably the main reason, further multi-well transverse analysis and comparison are needed, a perforation scheme is optimized, average extension of cracks is realized, and perforation cluster statistics that the difference between the liquid yield percentage of each cluster and the average contribution ratio of each section is more than 25% are shown in table 3.
TABLE 3 Table 3
| Large cluster number of yield contribution difference | Percentage of the whole perforation cluster | |
| Production system I | 44 | 69％ |
| Production system II | 35 | 55％ |
| Production system III | 26 | 41％ |
By comparing the liquid production conditions of each cluster under three production systems with comprehensive explanation and fracturing scale, as shown in fig. 22, the liquid production contribution of the layer near the target point of the horizontal section B of the well is lower, the liquid production contribution of the middle section and the rear part of the horizontal section is higher, the well yield is basically consistent with the well logging explanation result, and the increase of the fracturing scale is beneficial to increasing the yield.
2. Oil production ratio of each cluster under three production systems
The oil production conditions of each cluster under the three production systems were comprehensively analyzed, and fig. 23 shows the oil production contribution rate of each cluster under the three production systems. Under three production systems, the horizon with lower oil production contribution rate is mainly concentrated near the target point of the horizontal segment B, and the oil production and the quality of the reservoir form a positive correlation.
3. The water ratio of each cluster under three production systems
The water yield of each cluster under three production systems was analyzed comprehensively, and fig. 24 shows the water yield contribution rate of each cluster under three production systems. Under three production systems, the layer with lower water production is mainly concentrated at the middle and rear parts of the horizontal section, oil and water of each cluster of the well are discharged simultaneously, and the layer with lower oil production at the bottom has low water production.
4. Different types of reservoirs under three production systems are provided with oil production contribution rate and liquid production contribution rate
According to the lithology, physical properties and oiliness of the horizontal-section reservoir, the reservoir is divided into three main types, wherein the reservoir of the type I of the well is 389m, and the total number of the reservoir is 28 clusters; class II reservoirs are 656m long for a total of 35 clusters. According to different reservoir types, the oil production contribution rate and the liquid (oil+water) contribution rate of various reservoir types under three different production systems are obtained through statistics, and are shown in tables 4 and 5.
TABLE 4 oil production contribution rates for different types of reservoirs
| Class I reservoir oil production contribution rate | Type II reservoir oil production contribution rate | Contribution rate of oil production of III-class reservoir | |
| First production system | 52.2％ | 45.8％ | 2.0％ |
| Second production system | 62.0％ | 37.0％ | 1.0％ |
| Third production system | 64.8％ | 34.4％ | 0.9％ |
TABLE 5 liquid contribution rates for different types of reservoirs
| Contribution rate of liquid production of type I reservoir | Contribution rate of liquid produced by type II reservoir | Contribution rate of liquid production of III-class reservoir | |
| First production system | 66.9％ | 32.1％ | 1.0％ |
| Second production system | 64.2％ | 28.5％ | 7.3％ |
| Third production system | 59.3％ | 39.8％ | 0.8％ |
Under the monitoring of three production systems, the oil yield and the quality of the reservoir are in positive correlation, and the oil yield contribution of the class I reservoir is higher. Under three production systems, the average oil production contribution rate of the class I reservoir is 59.6 percent, and the average oil production contribution rate of the class II reservoir is 39.1 percent; the average liquid production contribution rate of the class I reservoir is 63.5 percent, and the average liquid production contribution rate of the class II reservoir is 33.5 percent
4. Liquid production conditions of each section under three production systems
FIG. 25 shows the liquid production contribution rates of the XX well in each section under three production systems, and the production layer section with higher liquid production rate of the well is concentrated in the middle of the horizontal section. Under the first production system, the liquid yield of the 14 th stage is highest, and the contribution rate is 14.65%; the liquid yield of the 15 th stage is highest under the second production system and accounts for 17.23%; the liquid yields of the 8 th section and the 20 th section under the third production system are the highest and respectively 11.33% and 11.09%.
(10) Fine temperature profile monitoring of full shaft and complete shape evaluation of shaft
The optical fiber distributed temperature measurement adopted in the production monitoring can obtain the temperature information of every 0.5m underground, and truly reflects the temperature profile of the whole shaft. According to the monitoring result, the temperature of 2680m of the well body of the well is 80.1 ℃, and the fine temperature profile of the whole well body of the XX well is shown in figure 26.
As can be seen from fig. 27, in the straight well section, the temperature change is basically consistent, the regional temperature gradient change is met, the straight well section has well complete well shape and no leakage point; in the horizontal section, except the perforating section, no obvious temperature change fluctuation exists, and the well bore of the horizontal section has good integrity. In summary, the wellbore is well-formed.
(11) Evaluation of fracturing Effect
The target layer of the horizontal section of the XX well is FI5, the drilling depth is 2750m, the horizontal section is 1106m, the drilling rate of sandstone 1085m is 98.1%, the drilling rate of sandstone 1046m is 94.6%. Adopting bridge plug to cut and volumetric fracture 23 sections 64 clusters, completing the fracturing construction within 7 months and 6 days-15 days, and setting the whole-well construction displacement to 8.5-13.0m 3 Per min, adding acid 168m 3 Well-logging total fracturing fluid 20283m 3 Sand 1952m 3 . The fracturing construction statistics are shown in table 6.
Table 6XX well fracturing design and construction parameter statistics
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In order to avoid channeling, the half length of the split joint is 1-5 sections, the length of the split joint is 100m, the length of the split joint is 6-9 sections, the size of the split joint is 10-23 sections, and the split joint is 400m. The specific design is as follows:
1 st to 5 th stage, the liquid volume is 600m 3 Proppant 60m 3 The half-slit length is 200m.
Sections 6-8, liquid amount 417m 3 Proppant 36m 3 The half-slit length is 100m.
Stage 9, liquid amount 291m 3 Proppant 24m 3 The half-slit length is 100m.
10,11,21,22 liquid level 782m 3 Proppant 80m 3 The half slit length is 400m.
Liquid amount of sections 12-20 and 23 1097m 3 Proppant 120m 3 The half slit length is 400m.
And comparing the fracturing transformation means with the actual yield of each section under three working systems, and searching the relation between the fracturing transformation method and the yield.
From the trend, the construction scale and the post-pressure productivity show obvious correlation, the well fracturing reconstruction construction is basically consistent with the fracturing design, and the crack extension length is effectively controlled from the viewpoint of the yield in the 1 st to 9 th sections, so that the communication with an adjacent well caused by excessive reconstruction is prevented to a certain extent. The well fracturing reconstruction construction is basically consistent with the fracturing design, and the crack extension length of the 1 st to 9 th sections is effectively controlled from the standpoint of yield, so that communication with an adjacent well caused by excessive reconstruction is prevented to a certain extent.
The foregoing description is only illustrative of the present invention and is not intended to limit the scope of the invention, and all equivalent structures or equivalent processes or direct or indirect application in other related technical fields are included in the scope of the present invention.
Claims (3)
1. A process for monitoring oil and gas well production by means of optical fiber sensing, the process comprising:
step a, equipment placement, installation and pressure test; the method specifically comprises the following steps: laying impermeable cloth on the ground, and placing a coiled tubing operation vehicle so that the central shaft of the roller is opposite to the wellhead and is 20-30m away from the wellhead; the fracturing truck and the ground pipeline are connected by the fracturing unit, the ground pipeline is matched for pressure test, the pump pressure is gradually increased during the pressure test, the pump is immediately stopped for pressure relief and correction when leakage is found, the pressure test is 3.5MPa, the pressure is stabilized for 5min, the pressure is stabilized for 15min when no leakage is pressurized to 45MPa, and the pressure drop is smaller than 0.5 MPa;
step b, testing tools; the method specifically comprises the following steps: hoisting the injection head and the blowout preventer to a designated position by using a crane, and connecting a power pipeline for functional test; operating all levels of flashboards of the BOP, observing that all levels of flashboards are normally opened and closed from the top end of the BOP and the indication pins, and corresponding to the operation actions; after the test is qualified, all flashboards of the BOP are positioned at the full-open position; connecting a coiled tubing guide using a special tool; inserting a coiled tubing into the injector head; connecting a continuous oil pipe connector and a test pulling/pressure testing disc, testing the tensile strength of a continuous oil pipe joint by pulling a filling head, wherein the joint part and the continuous oil pipe body do not slide, and the joint and the pin are not damaged; connecting a blowout preventer to a wellhead; the lubricator is connected to the lower part of the blowout preventer, and the lubricator is connected to the blowout preventer; a guy rope is tied, so that the wellhead device is ensured to have no shaking; closing the blowout prevention box, and pressing the blowout prevention box, the blowout prevention pipe and the blowout preventer through the coiled tubing to connect, seal and test pressure; closing the totally-enclosed flashboard, and performing pressure test on the totally-enclosed flashboard of the blowout preventer from fracturing eight-way pressing; opening the fully-sealed flashboard, enabling an oil pipe to descend below the blowout preventer half-sealed flashboard, closing the blowout preventer half-sealed flashboard, and performing pressure test on the blowout preventer half-sealed flashboard through continuous oil pipe pressing;
step c, data acquisition; the method specifically comprises the following steps: installing a well tool, and testing the performance of the tool; when the injector is in a well, the injector head directional control rod is slowly operated to lower the oil pipe; lifting the continuous oil pipe after the continuous oil pipe is lowered to a specified depth, so that the continuous oil pipe is in a stretching state; the injection head and the roller brake, the equipment maintains pressure, and the system pressure and the well casing pressure of the equipment are monitored; notifying an optical monitoring person to perform the next construction; after all data acquisition is completed, after the data acquisition is completed, starting to lift the continuous oil pipe and lifting out of the wellhead;
step d: analyzing data; the method specifically comprises the following steps: forward modeling: simulating an unknown parameter by using a plurality of known parameters, specifically using Production Inverse software to analyze according to well structure, well completion information, stratum property, production data and parameters in the well, thereby establishing various wellbore fluid types and thermodynamic models, and selecting one of the wellbore fluid types, namely obtaining a geothermal curve, namely a curve with temperature change along with the increase of well depth;
reverse modeling: by utilizing data software, the data of the DTS temperature is input into the software, the software simulates energy change caused by fluid flow below each point in a well bore and energy change caused by formation fluid output, and then the output of each cluster is simulated and calculated, and a plurality of DTS temperature-output change curves are obtained through simulation;
the reverse modeling analysis result is brought into forward modeling, if the data comparison result passes, the next step is carried out, if the result does not pass, the reverse modeling is repeated until the forward and reverse modeling data are deduced to be consistent;
and (3) data modeling eliminates multiple solutions, and simultaneously performs error analysis and quality control.
2. The process for monitoring oil and gas well production by using optical fiber sensing means as claimed in claim 1, wherein: the step c of collecting specifically comprises the following steps: (1) Closing the well, collecting a ground temperature baseline, and taking an average result of data in the closing period as the ground temperature baseline in data analysis;
(2) Opening a well, and carrying out production monitoring of a first production system: a certain amount of oil-water enters a shaft from a stratum to cause different temperature changes, and an optical fiber in a continuous oil pipe can continuously record a temperature process;
(3) Changing production monitoring of the second production system, and recording the change condition of the whole well temperature after the change of the bottom hole differential pressure;
(4) And closing the well, and measuring a ground temperature return baseline.
3. The process for monitoring oil and gas well production by using optical fiber sensing means as claimed in claim 1, wherein: the equipment in the step c comprises a DTS optical transceiver and an OTDR instrument, wherein the DTS optical transceiver is utilized to polish the optical fiber coiled tubing, the optical fiber is used as a sensing sensitive element and a transmission signal medium, and the OTDR instrument is utilized to detect disturbance information of external signals distributed on the sensing optical fiber.
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