CN115713036A - Method for predicting blowout stop time of flowing well and method for optimizing size of oil nozzle - Google Patents
Method for predicting blowout stop time of flowing well and method for optimizing size of oil nozzle Download PDFInfo
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Abstract
The invention discloses a blowout stop time prediction method and a choke size optimization method for a flowing well. The invention overcomes the defect that the dynamic analysis of the flowing well can not be predicted for a long time because the parameters of the shaft and the oil reservoir are considered as static parameters in the traditional method, and simultaneously overcomes the defects that the size adjustment of the oil nozzle in the flowing period of the oil well depends on the experience of people and the workload of manpower is large.
Description
Technical Field
The invention relates to the technical field of oil exploitation, in particular to a method for predicting blowout stop time of a flowing well and a method for optimizing size of an oil nozzle.
Background
The blowout-stopping time of the flowing well is predicted, the production system of the oil well is optimized by combining production requirements, the optimized production system of the single well can enable the well to prolong the blowout oil production period or enable the single well to accumulate oil during the blowout oil production period to be the maximum, when the oil field is developed in an early stage, the stratum energy is sufficient, the oil well can form the blowout, the blowout of the flowing well can be stopped as the extraction degree is increased, the stratum pressure is reduced, the water content is increased, and the blowout of the flowing well can be stopped, so that the oil can be extracted by adopting a reasonable artificial lifting method. The method has the advantages that the blowout time of the blowout well can be scientifically predicted, support can be provided for adjusting the lifting mode, ensuring normal operation and the like, meanwhile, the blowout time of the oil well can be accurately predicted by analysis under actual production conditions such as oil field production and output limitation, the working condition of the oil well is optimized to avoid premature blowout stop of the oil well, and the method has important significance in oil reservoir dynamic analysis at the initial stage of oil field development scheme design and after production.
At present, the main methods for predicting the blowout stop time of the flowing well at home and abroad comprise: minimum bottom hole flow pressure method, minimum wellhead oil pressure method and minimum stratum pressure prediction method.
For the minimum formation pressure method: uncertainties exist in formation pressure prediction, reservoir energy variation, and pipe flow model selection. The research on a self-blowout-stopping time prediction method of a self-blowout well in a Hafa sub-oil field of the Yang military (2019) is used for predicting the self-blowout-stopping time of the self-blowout well based on a multi-phase pipe flow and node analysis technology and simultaneously considering the water content and the change of the formation pressure.
For the minimum downhole pressure method: uncertainties exist in the aspects of pipe flow formulas, model selection and the like, and the prediction results can be directly influenced by the lack of reservoir data and the inaccuracy of numerical simulation results. Huang's rays (1997) considers the water content, the fluid density, the crude oil volume coefficient, the oil layer depth, the degassing point depth, the friction coefficient and the crude oil saturation pressure in the practical oil reservoir engineering and dynamic analysis method, establishes the vertical well jet stopping flow pressure calculation empirical formula suitable for the oil reservoir in China, and calculates the jet stopping time by combining the prediction of the bottom hole flow pressure. The method adopts an empirical formula to calculate the stopped jet pressure of the vertical well, the application of the empirical formula is greatly limited, the production trend of the bottom hole jet pressure prediction in a specific period is based, and the prediction in a longer period has larger uncertainty. The 'establishment of a flowing pressure prediction model at the bottom of a flowing well by using a neural network' of Liu Xiangping and the 'application of a BP neural network in the flowing pressure prediction at the bottom of the flowing well by using Gong crystal' utilize the high nonlinear mapping capability of the neural network, the method predicts the bottom hole pressure by training the relationship between characteristic parameters such as a choke, depth, oil production, gas production, water production, oil pressure casing pressure, crude oil density and the like and an actually measured value of the bottom hole pressure, depends on the quality and the quantity of training samples, and is difficult to establish the relationship between the bottom hole pressure prediction and the blowout stop time prediction.
For the minimum wellhead pressure method: only for the case of a particular nozzle tip size. The prediction of the blowout stopping time of the Elake Ahdeb oil field in Wangqinghua considers that under the condition that the size of an oil nozzle is not changed, the oil pressure of a well head is exponentially reduced along with the time, and the blowout stopping time of the oil well is obtained by fitting the pressure reduction trend of the well head. Since wellhead pressure is affected by production allocation, the method considered exponential decrease is not typical.
Therefore, the method for predicting the blowout stop time of the flowing well cannot predict the dynamic analysis of the flowing well for a long time, and cannot overcome the defects that the size adjustment of the oil nozzle in the flowing period of the oil well depends on the experience of people and the workload of manpower is large.
Disclosure of Invention
The invention aims to provide a blowout stop time prediction method and a choke size optimization method for a flowing well, which fully consider the influences of formation pressure, water content, gas-oil ratio, fluid physical properties, oil pipe friction resistance, productivity index and the like, predict the blowout stop time of an oil well under a current choke on the basis of considering a time-varying inflow dynamic curve and time-varying on-way pressure loss, and automatically obtain the size of an optimized oil production choke of the oil well by combining the longest flowing period time and the single-well accumulated oil production target.
In order to achieve the technical purpose, the invention is realized by the following technical scheme:
a blowout stop time prediction method for a flowing well comprises the following steps:
(1) Fitting the pressure test data of the oil well at different moments to obtain uncertain parameters U of the properties of the shaft and the fluid at different test moments i I =1,2,.., i,. K, k is the total number of tests;
(2) Analyzing the relation between the accumulated oil production and the uncertain parameters according to the uncertain parameter fitting values of different test moments obtained in the step (1) and establishing a target well uncertain parameter prediction model;
(3) Establishing a regression model between the formation pressure and the accumulated liquid production amount according to a substance balance principle, and calculating and predicting the formation pressure of the oil well;
(4) Combining the uncertain parameter prediction model of the target well and the actual production data of the oil well, accurately calculating the bottom hole pressure of the oil well at different moments, then combining the real-time prediction of the formation pressure of the oil well, calculating the historical productivity index of the oil well, and establishing an oil well productivity index prediction model;
(5) Establishing a regression equation by combining historical data of the water content of the oil well to obtain a prediction model of the water content of the oil well;
(6) And (3) establishing oil well inflow dynamic models under different accumulated productions by combining the prediction models obtained in the steps (2), (3), (4) and (5) for calculating oil well bottom pressure under different accumulated oil production, establishing oil well wellhead pressure prediction models under different accumulated productions by combining uncertain parameters of shaft and fluid properties for calculating oil well wellhead pressure under different oil production under corresponding accumulated oil production, and solving a spray stopping time prediction model by combining an oil nozzle flow model for obtaining the spray stopping time of the oil well.
Further, the oil well pressure test data at different times in the step (1) includes a pressure profile W i p Temperature profile W i t And fluid density profile W i d The step (1) further comprises a wellbore profile calculation model F 1 Calculating model F from wellbore profile 1 Establishing a deterministic parameter P i And the uncertain parameter U of the properties of the shaft and the fluid at different testing moments is subjected to the following Bayesian optimization formula i Carrying out optimization and adjustment:
wherein: omega p 、ω t 、ω d Fitting loss function weights for the pressure profile, the temperature profile, and the fluid density profile, respectively;
respectively, the parameter P given in the i-th test i And U i According to F 1 And (4) calculating a pressure profile, a temperature profile and a fluid density profile by using the model.
Further, optimizing the uncertain parametersU i And cumulative oil production NP at the i-th test i (i =1, 2.. I.. K) to obtain the uncertainty parameter prediction model of the target well as U = F 2 (NP) wherein F 2 (. Cndot.) is the cumulative oil production NP and uncertainty parameter U obtained by regression equation i Is measured as a function of (c).
Further, the regression model between the formation pressure and the liquid production capacity in step (3) is as follows:
where m is fit to the equation through the actual production data of the well
Obtaining;
mean formation pressure in psi; p is a radical of formula i Initial formation pressure in psi; r is e Is the well control radius in m; phi is porosity and is dimensionless; h is the perforation thickness in m; c. C t Is the comprehensive compression coefficient with the unit of 1/Mpa, p wf Is the bottom hole flow pressure in psi.
Further, the model is predicted according to uncertain parameters U = F 2 (NP) with defined real-time production parameters
Model F is calculated by wellbore profile 1 Obtaining the well bottom flowing pressure historical data
And calculating the real-time productivity index of the oil well by combining a regression model between the formation pressure and the accumulated liquid production amount and the actual oil production amount data
Obtaining a productivity index prediction model P I ＝F 3 (NP) wherein P I To produceEnergy index, unit m 3 /d·Mpa；F 3 (. Is) is prepared by { (P) I，k ，NP k ) P regression of the series of data (k =1,2, \ 8230;) I And NP.
Further, the water cut prediction model of the oil well in the step (5) is f w And = alnNP + b, where a and b are the slope and intercept, respectively, and are both production fit parameters for the pre-well data.
Further, the inflow dynamic models of the oil wells under different accumulated productions in the step (6) are inflow dynamic models of Petrobras three-phase flow, and corresponding oil well inflow dynamic curves p are drawn by calculating the bottom hole pressures of the oil wells under different accumulated oil production quantities wf (q o ，N P ) In which P is wf Bottom hole flow pressure in psi; q. q.s o For oil production, unit STB;
the oil well wellhead pressure prediction model combines an oil well water content prediction model, an uncertain parameter prediction model and a shaft profile calculation model F 1 Calculating the pressure p of the oil well mouth under different oil production quantities under the corresponding accumulated oil production quantity wh And drawing a pressure curve p of the oil well wellhead wh (q o ，NP)；
The nozzle flow model is
Wherein the logarithm of both sides is taken to obtain ln (p) wh )＝ln(a 1 )+a 2 ln(GOR)-a 3 ln(d)+ln(q 0 )+a 4 ln(1-f w ) GOR is the ratio of produced gas to oil, d is the size of oil nozzle, f w Is the water content, a 1 、a 2 、a 3 And a 4 For well head pressure p wh GOR (gas-oil ratio), nozzle size d and oil production q o And water content f w Parameters obtained after fitting historical production data, and water content f w The produced gas-oil ratio GOR is a function of the accumulated oil production, so the nozzle flow model formula can be recorded as
The spray stopping time prediction model is
Obtaining the current nozzle size d 0 A shut-off time T of (1), wherein p wh,min To incorporate the minimum wellhead pressure required for export, in psi.
After the predicted oil well blowout stopping time and the single well accumulated oil production target are combined, an oil well blowout stopping oil nozzle size optimization model is established to obtain the optimized oil nozzle size.
Further, the size optimization model of the oil nozzle for stopping the oil well injection is
For obtaining maximum cumulative oil production NP max And outputting the optimized size of the oil well nozzle.
Compared with the prior art, the invention has the following advantages and beneficial effects:
the method for predicting the blowout stop time of the flowing well fully considers the influences of formation pressure, water content, gas-oil ratio, fluid physical property, oil pipe friction resistance, productivity index and the like, predicts the current blowout stop time of the oil well under the oil nozzle on the basis of considering a time-varying inflow dynamic curve and time-varying on-way pressure loss, and overcomes the defect that the dynamic analysis of the flowing well can not be predicted for a long time due to the fact that parameters of a shaft and an oil reservoir are considered as static parameters in the traditional method; and after the longest blowout period time and the accumulated oil production target of a single well are combined, an optimized blowout period oil nozzle size is obtained by establishing an oil nozzle size optimization model for stopping the blowout of the oil well, and the defects that the adjustment of the oil nozzle size of the oil well in the blowout period depends on human experience and the manual workload is large are overcome.
Drawings
In order to more clearly illustrate the technical solutions of the exemplary embodiments of the present invention, the drawings that are required in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and that those skilled in the art may also derive other related drawings based on these drawings without inventive effort. In the drawings:
FIG. 1 is a Bayesian algorithm framework for target well pressure profile fitting provided by embodiments of the present invention;
FIG. 2 is a schematic diagram of a target well pressure profile before and after fitting provided by an embodiment of the present invention;
FIG. 3 is a model for predicting uncertain parameters of a target well according to an embodiment of the present invention;
FIG. 4 is a model for predicting formation pressure in a target well according to an embodiment of the present invention;
FIG. 5 is a real-time bottom-hole pressure calculation for a target well provided by embodiments of the present invention;
FIG. 6 is a model for predicting a target well productivity index provided by an embodiment of the present invention;
FIG. 7 is a target well water cut prediction model provided by an embodiment of the invention;
FIG. 8 is a well inflow dynamic model for different cumulative productions of a target well according to an embodiment of the present invention;
FIG. 9 is a graph of the different cumulative inflow dynamics and wellhead pressures for an oil well according to an embodiment of the present invention;
FIG. 10 is a model of a target well choke flow provided by an embodiment of the present invention;
fig. 11 is a schematic diagram of a process for optimizing the size of a target well nozzle according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail below with reference to examples and the accompanying drawings, and the exemplary embodiments and descriptions thereof are only used for explaining the present invention and are not used as limiting the present invention.
Examples
A method for predicting the blowout stop time of a flowing well and a method for optimizing the size of an oil nozzle comprise the following steps:
(1) Fitting the pressure test data of the oil well at different moments to obtain uncertain parameters U of the properties of the shaft and the fluid at different test moments i I =1,2,. I,. K, k being the total number of tests;
(2) Analyzing the relation between the accumulated oil production and the uncertain parameters according to the uncertain parameter fitting values of different test moments obtained in the step (1) and establishing an uncertain parameter prediction model of the target well;
(3) According to the principle of material balance, establishing a regression model between the formation pressure and the accumulated liquid production amount for calculating and predicting the formation pressure of the oil well;
(4) Combining the uncertain parameter prediction model of the target well and the actual production data of the oil well, accurately calculating the bottom hole pressure of the oil well at different moments, then combining the real-time prediction of the formation pressure of the oil well, calculating the historical productivity index of the oil well, and establishing an oil well productivity index prediction model;
(5) Establishing a regression equation by combining historical data of the water content of the oil well to obtain a prediction model of the water content of the oil well;
(6) And (4) establishing oil well inflow dynamic models under different accumulated productions by combining the prediction models obtained in the steps (2), (3), (4) and (5) for calculating the bottom pressure of the oil well under different accumulated oil production quantities, establishing oil well wellhead pressure prediction models under different accumulated productions by combining uncertain parameters of a shaft and fluid properties for calculating the oil well wellhead pressures under different oil production quantities under corresponding accumulated oil production quantities, and solving a blowout stop time prediction model by combining an oil nozzle flow model for obtaining the blowout stop time of the oil well.
Uncertain parameters U of the properties of the shaft and the fluid at different test moments in the step (1) i I =1,2, a, i, k being the total number of tests, it is necessary to first determine a set U of uncertainty parameters affecting the on-way pressure, temperature and fluid density during the flow of the fluid along the wellbore from the reservoir to the wellhead, the main uncertainty parameters selected by the invention including: shaft friction f, crude oil density ρ o Crude oil viscosity μ o Natural gas density ρ g Water density of formation ρ w Crude oil saturation pressure p b Heat transfer coefficient lambda, crude oil specific heat c o Specific heat of formation water c w Specific heat of natural gas c g I.e. U = { f, ρ o ,μ o ,ρ g ,ρ w ,p b ,λ,c o ,c w ,c g }。
Then combining with a shaft section calculation model F 1 In the embodiment, a Beggs-Brill model is selected to calculate a wellbore profile, and a deterministic parameter set P is established, wherein the deterministic parameter set P comprises: well depth h and inner diameter r of oil pipe t Inner diameter r of casing c Pressure p at the well head wh (reservoir pressure), well head temperature t wh (oil reservoir temperature) and daily oil production q o Daily water yield q w Daily gas production q g And the gradient of the earth temperature g t I.e. P = { h, r t ,r c ,p wh ,t wh ,q o ,q w ,q g ,g t }。
After the uncertainty parameter set U is obtained, the uncertainty parameter U of the oil well in the ith flowing pressure test is optimized and adjusted through a Bayesian optimization algorithm shown in figure 1 i And then the pressure profile W obtained by testing the pressure of the oil well at different moments i p Temperature profile W i t And fluid density profile W i d The fitting was performed as shown in fig. 2, with the following fitting equation:
Obtaining uncertain parameters U in the ith flow pressure test by the fitting optimization of the formula i 。
In the step (2), uncertain parameters U of fitting optimization of the oil well multiple flowing pressure test are obtained i (i =1, 2.. I.. K) (k is the flow pressure profile test)Total number of times) and cumulative oil production NP at the i-th test i (i =1, 2.. I.. K) constitutes a series of point sets { (U) i ，NP i ) H (i =1,2,. I, i.. K), and then (U) { (U) } i ，NP i ) Using Least squares (OLS) to perform fitting regression (i =1, 2.. Times, i.. Times k) to obtain uncertain parameter prediction model U = F 2 (NP) of F 2 (. I) the cumulative oil production NP obtained by regression with an uncertainty parameter U i The uncertain parameter prediction model of the uncertain parameters such as the viscosity of crude oil, the friction coefficient, the multiple of gasoline ratio and the saturation pressure along with the change of the accumulated oil quantity is obtained in the mode shown in the figure 3.
After the uncertain parameter prediction model is determined, step (3) is carried out to obtain a regression model between the formation pressure and the accumulated liquid production according to the substance balance principle
The results are shown in FIG. 4, where m is fit to the equation through the actual production data of the well
Obtaining;
mean formation pressure in psi; p is a radical of i Initial formation pressure in psi; r is e Is the well control radius in m; phi is porosity and is dimensionless; h is the perforation thickness in m; c. C t Is the comprehensive compression coefficient with the unit of 1/Mpa, p wf Is the bottom hole flow pressure in psi.
Obtaining uncertain parameter prediction model U = F according to the steps (1) and (2) 2 (NP) with known determined real-time production parameters
Obtaining the well bottom flowing pressure historical data through a shaft profile calculation model Beggs-Brill model
The result is shown in fig. 5, and the real-time productivity index of the oil well is calculated by further combining the regression model between the formation pressure and the accumulated liquid production obtained in the step (3) and the actual oil production data
Obtaining a productivity index prediction model P I ＝F 3 (NP), as shown in FIG. 6; wherein P is I Is a productivity index, in m 3 /d·Mpa；F 3 (. Is) is prepared by { (P) I，k ，NP k ) P from regression of the series of data (k =1,2, \8230;) I And NP.
According to historical data of the water content and the accumulated oil production in the blowout period of the oil well, performing regression analysis on the known historical data by using a Least Square method (OLS), and obtaining a water content prediction model f in the blowout period of the oil well w The water cut values of the oil well at different oil production degrees can be predicted by = alnnnp + b, and the results obtained in this example are shown in fig. 7, where a and b are the slope and intercept mathematically, respectively, and the production is the fitting parameters for the previous data of the oil well.
Combining the obtained uncertain parameter prediction model, the regression model between the formation pressure and the accumulated liquid production amount, the productivity index prediction model and the oil well water content prediction model in the calculation, introducing a Petrobras three-phase flow inflow dynamic model for carrying out long-term prediction on the dynamic analysis of the flowing well, calculating the bottom hole pressure of the oil well under different accumulated oil production amounts, and drawing a corresponding oil well inflow dynamic curve p wf (q o NP), the results are shown in FIG. 8, where P wf Bottom hole flow pressure in psi; q. q of o For oil production, unit STB.
In order to make the result more accurate, the water content prediction model, the uncertain parameter prediction model and the shaft section calculation model F of the oil well are further combined after the bottom hole pressure of the oil well under different accumulated oil production quantities is obtained 1 Calculating the pressure P of the oil well mouth under different oil production quantities under the corresponding accumulated oil production quantity wh In psi and plotting the well head pressure curve p of the well wh (q o NP), the results of which are shown in fig. 9; and further introducing a nozzle tip flow pattern
The optimization was performed, and the result is shown in FIG. 10, where ln (p) was obtained by logarithmically reducing both sides wh )＝ln(a 1 )+a 2 ln(GOR)-a 3 ln(d)+ln(q 0 )+a 4 ln(1-f w ) GOR is the ratio of produced gas to oil, d is the size of oil nozzle, unit m, oil production q o The unit STB, f w Is the water content, and a 1 、a 2 、a 3 And a 4 For the above-mentioned parameter oil well head pressure p wh And produced gas-oil ratio GOR, nozzle size d and oil production q o And water content f w The historical production data is obtained after fitting by adopting a least square method, and in addition, the water content f w The produced gas-oil ratio GOR is a function of the accumulated oil production, so the nozzle flow model formula can be recorded as
Substituting the obtained result into a spray stopping time prediction model
Obtaining the current nozzle size d 0 A spray-off time T of, wherein p wh,min To incorporate the minimum wellhead pressure required for export, in psi.
Oil nozzle size optimization model for further considering oil well stop before obtaining more accurate oil well oil nozzle size
Obtaining the maximum cumulative oil production NP max In this embodiment, in order to obtain the nozzle size efficiently, the optimized nozzle size during the self-blowing period is automatically output by the stored computer pseudo code program, and the result is shown in fig. 11, where the pseudo code program is as follows:
Set p wh,min
While(NP i ＜＝NP max )：
While(p wh,i ＞p wh,min )：
NP i ＝NP i +q o，i
d i ＝d i -δ
wherein, delta is the minimum amount of the size adjustment of the oil nozzle, and q is o,i Is the daily oil production at the i-th test, p wh,i The wellhead pressure of the oil well at the ith test. In particular, in FIG. 11, when the wellhead pressure curve is plotted against the choke size d i The intersection point (point A) of the lower nozzle flow model curve is equal to the minimum required wellhead pressure p wh,min Then, continue to use the nozzle tip d i The injection is stopped, so the size of the oil nozzle needs to be adjusted, the adjustment amount is delta every time, and the wellhead pressure corresponds to a point P at the moment 2 The pressure loss of the wellbore is corresponding to P 1 P 2 Ordinate d of i The size of the oil nozzle is continuously reduced and adjusted, the self-injection oil extraction period of the oil well is prolonged, and when the accumulated production of the oil well reaches NP max When the maximum value of the wellhead pressure curve is equal to the minimum required wellhead pressure p wh,min At time (point B), the well completely lost its flow capability.
The electronic device carrying the computer pseudo code program can be a desktop computer, a notebook, a palm computer, a cloud server and other computing devices. The electronic device may include, but is not limited to, a processor. Those skilled in the art will appreciate that the schematic diagrams are merely examples of an electronic device, and do not constitute a limitation of an electronic device, and may include more or fewer components than those shown, or some components may be combined, or different components, e.g., the electronic device may further include an input output device, a network access device, etc.
The Processor may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. The general purpose processor may be a microprocessor or the processor may be any conventional processor or the like, the processor being the control center for the electronic device and various interfaces and lines connecting the various parts of the overall electronic device.
The memory may be used to store the computer programs or modules, and the processor may implement various functions of the electronic device by executing or executing the computer programs or modules stored in the memory and calling data stored in the memory. The memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required by at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data (such as audio data, a phonebook, etc.) created according to the use of the cellular phone, etc. Further, the memory may include high-speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a flash memory card (FlashCard), at least one magnetic disk storage device, a flash memory device, or other volatile solid state storage device.
The electronic device integrated unit, if implemented in the form of a software functional unit and sold or used as a separate product, may be stored in a computer readable storage medium. Based on such understanding, all or part of the flow in the method according to the embodiments of the present invention may be realized, or the related hardware may be instructed to be incomplete through a computer program, where the computer program may be stored in a computer readable storage medium, and when the computer program is executed by a processor, the steps of the embodiments of the method may be realized.
Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, read-Only Memory (ROM), random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, etc. It should be noted that the computer-readable medium may contain suitable additions or subtractions depending on the requirements of legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer-readable media may not include electrical carrier signals or telecommunication signals in accordance with legislation and patent practice.
The above-mentioned embodiments are intended to illustrate the objects, technical solutions and advantages of the present invention in further detail, and it should be understood that the above-mentioned embodiments are merely exemplary embodiments of the present invention, and are not intended to limit the scope of the present invention, and any modifications, equivalent substitutions, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (10)
1. A blowout stop time prediction method for a flowing well is characterized by comprising the following steps:
(1) Fitting the pressure test data of the oil well at different moments to obtain uncertain parameters U of the properties of the shaft and the fluid at different test moments i I =1,2,.., i,. K, k is the total number of tests;
(2) Analyzing the relation between the accumulated oil production and the uncertain parameters according to the uncertain parameter fitting values of different test moments obtained in the step (1) and establishing an uncertain parameter prediction model of the target well;
(3) Establishing a regression model between the formation pressure and the accumulated liquid production amount according to a substance balance principle, and calculating and predicting the formation pressure of the oil well;
(4) Combining the uncertain parameter prediction model of the target well and the actual production data of the oil well, accurately calculating the bottom hole pressure of the oil well at different moments, then combining the real-time prediction of the formation pressure of the oil well, calculating the historical productivity index of the oil well, and establishing an oil well productivity index prediction model;
(5) Establishing a regression equation by combining historical data of the water content of the oil well to obtain a prediction model of the water content of the oil well;
(6) And (3) establishing oil well inflow dynamic models under different accumulated productions by combining the prediction models obtained in the steps (2), (3), (4) and (5) for calculating oil well bottom pressure under different accumulated oil production, establishing oil well wellhead pressure prediction models under different accumulated productions by combining uncertain parameters of shaft and fluid properties for calculating oil well wellhead pressure under different oil production under corresponding accumulated oil production, and solving a spray stopping time prediction model by combining an oil nozzle flow model for obtaining the spray stopping time of the oil well.
2. The method for predicting blowout stop time of a flowing well according to claim 1, wherein the pressure test data of the oil well at different moments comprise a pressure profile W i p Temperature profile W i t And fluid density profile W i d The step (1) further comprises a wellbore profile calculation model F 1 Calculating model F from wellbore profile 1 Determining a deterministic parameter P i And the uncertain parameter U of the properties of the shaft and the fluid at different testing moments is subjected to the following Bayesian optimization formula i Carrying out optimization and adjustment:
wherein: omega p 、ω t 、ω d Fitting loss function weights for the pressure profile, the temperature profile, and the fluid density profile, respectively;
respectively, the parameter P given in the i-th test i And U i According to F 1 And (4) calculating a pressure profile, a temperature profile and a fluid density profile by using the model.
3. The flowing well blowout stop time prediction method according to claim 2, characterized in that uncertainty parameter U is obtained by i And cumulative oil production NP at the i-th test i (i =1, 2.. I.. K) to obtain the uncertainty parameter prediction model of the target well as U = F 2 (NP) wherein F 2 (. Is) the cumulative oil production NP and uncertainty parameter U obtained by regression equation i Is measured as a function of (c).
4. The method for predicting blowout time of a flowing well according to claim 3, wherein the regression model between the formation pressure and the accumulated liquid production is as follows:
where m is the fitting equation of the actual production data of the well
Obtaining;
mean formation pressure in psi; p is a radical of formula i Initial formation pressure in psi; r is a radical of hydrogen e Is the well control radius in m; phi is porosity and is dimensionless; h is the perforation thickness in m; c. C t Is the comprehensive compression coefficient in the unit of 1/Mpa, p wf Is the bottom hole flow pressure in psi.
5. The flowing well blowout stop time prediction method according to claim 4, characterized in that a model U = F is predicted according to uncertain parameters 2 (NP) with defined real-time production parameters
Model F is calculated by wellbore profile 1 Obtaining the well bottom flowing pressure historical data
And calculating the real-time productivity index of the oil well by combining a regression model between the formation pressure and the accumulated oil production amount and actual oil production data
Obtaining a productivity index prediction model P I ＝F 3 (NP) wherein P I Is a productivity index, in m 3 /d·Mpa；F 3 (. Is) is prepared by { (P) I，k ，NP k ) P regression of the series of data (k =1,2, \ 8230;) I And NP.
6. The blowout stop time prediction method of the flowing well according to claim 5, wherein the prediction model of the water content of the oil well in the step (5) is f w And = a ln NP + b, where a and b are the slope and intercept, respectively, and are both production fit parameters for the well lead data.
7. The flowing well blowout stop time prediction method according to claim 6,
the oil well inflow dynamic model under different cumulative production is a Petrobras three-phase flow inflow dynamic model, and a corresponding oil well inflow dynamic curve p is drawn by calculating the bottom hole pressure of the oil well under different cumulative oil recovery quantities wf (q o NP) where P wf Bottom hole flow pressure in psi; q. q.s o For oil production, unit STB;
the oil well wellhead pressure prediction model combines an oil well water content prediction model, an uncertain parameter prediction model and a shaft section calculation model F 1 Calculating the pressure p of the oil well mouth under different oil production quantities under the corresponding accumulated oil production quantity wh And drawing a pressure curve p of the oil well wellhead wh (q o ,NP)；
The nozzle flow model is
Wherein the logarithm of both sides can be taken to obtain ln (p) wh )＝ln(a 1 )+a 2 ln(GOR)-a 3 ln(d)+ln(q 0 )+a 4 ln(1-f w ) GOR is the ratio of produced gas to oil, d is the size of oil nozzle, f w Is the water content of a 1 、a 2 、a 3 And a 4 For well head pressure p of oil well wh Gas-oil ratio GOR, nozzle size d and oil production q o And water content f w Parameters obtained after fitting of historical production data, in addition, the water content f w The produced gas-oil ratio GOR is a function of the accumulated oil production, so the nozzle flow model formula can be recorded as
The spray stopping time prediction model is
Obtaining the current size d of the oil nozzle 0 A shut-off time T of (1), wherein p wh,min To incorporate the minimum wellhead pressure required for export, in psi.
8. A method for optimizing the size of a nozzle, characterized in that, in combination with the oil well blowout stop time predicted by the blowout stop time prediction method for a flowing well according to any one of claims 1 to 7 and the accumulated oil production target of a single well, a nozzle size optimization model for the blowout stop of the oil well is established, wherein the nozzle size optimization model for the blowout stop of the oil well is
For obtaining maximum cumulative oil production NP max And outputting the optimized size of the oil nozzle.
9. An electronic device, comprising:
a processor;
a memory for storing processor-executable instructions;
the processor is configured to execute the blowout stop time prediction method and the nozzle size optimization method of the flowing well in any one of claims 1 to 8.
10. A computer-readable storage medium, characterized in that,
the method comprises a stored computer program which is used for executing the method for predicting the blowout stop time of the flowing well and the method for optimizing the size of the oil nozzle in any one of claims 1 to 8 when running.
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| CN116698932B (en) * | 2023-08-01 | 2023-09-26 | 四川圣诺油气工程技术服务有限公司 | Microbial film monitoring sensor for pressurized pipeline |
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