CN107451325B - Method and device for real-time quantitative evaluation of failure risk of deep well and ultra-deep well fracturing casing - Google Patents
Method and device for real-time quantitative evaluation of failure risk of deep well and ultra-deep well fracturing casing Download PDFInfo
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Abstract
The embodiment of the application provides a method and a device for quantitatively evaluating failure risk of a deep well and ultra-deep well fracturing sleeve in real time, wherein the method comprises the following steps: determining the probability distribution of the pressure load and the compressive strength of the casing; determining the static failure probability of the casing according to the probability distribution; acquiring online working condition monitoring parameters of the casing according to a fracturing construction curve and a fracturing construction record, determining a value function between the online working condition monitoring parameters and the casing failure probability, and determining the dynamic failure probability of the casing in the fracturing process according to the value function; and constructing a risk evaluation matrix of the casing according to the static failure probability and the dynamic failure probability, and evaluating the failure risk of the casing according to the risk evaluation matrix. The method and the device can realize real-time quantitative evaluation of the failure risk of the fracturing sleeve of the deep well and the ultra-deep well.
Description
Technical Field
The application relates to the field of fracturing sleeve risk assessment in a shale oil and gas development process, in particular to a method and a device for quantitatively assessing failure risk of a deep well and ultra-deep well fracturing sleeve in real time.
Background
In the development process of the shale oil gas deep well ultra-deep well, the casing needs to bear a multiple-cycle repeated load spectrum, and particularly along with popularization and application of a high-pressure, large-displacement, continuous and equal-pressure fracturing operation mode, the probability of crushing deformation is greatly increased, subsequent operation is difficult, and the construction process is seriously influenced.
The traditional analysis and evaluation of the bearing capacity of the casing mainly adopts a safety factor method, and the ratio of the strength to the maximum load is compared with a preset safety factor to serve as a sign of safety. The defects are mainly shown in the following 4 aspects: firstly, taking strength and load parameters as fixed values, and not considering the random variation characteristic of casing parameters; the influence of the discrete degree and uncertainty of various parameters on the structure reliability is not analyzed, and the safety coefficient is not linked with the quantized sleeve reliability; the safety coefficient is determined by human experience, the subjective randomness is high, and the possibility that the safety reliability evaluation result of the sleeve is larger or smaller exists; fourthly, the construction operation causes the defects of abrasion, corrosion and the like, the bearing capacity of the sleeve is inevitably reduced, and the actual value of the safety coefficient cannot indicate the specific safety level and state.
In fact, due to the influence of manufacturing processes and technologies, and randomness of geometric parameters and mechanical performance parameters of the casing, uncertainty also exists in ground stress and formation parameters caused by a complex geological structure, and a large amount of unknown factors and parameter changes are difficult to be correctly processed by a traditional safety coefficient method. Starting from the 90 s of the 20 th century, based on the theory of structural reliability, a series of Quantitative Risk Analysis (QRA) is proposed at home and abroad, which is a main means for dealing with uncertainty factors of a casing at present. For example, some practical case scenarios applying QRA to casing design include 2 exploration wells and 1 development well, which indicates that QRA has the advantage of selecting an appropriate casing string when marginalized design factors are caused by the conventional method; some points show that the QRA is suitable for casing design of high-temperature and high-pressure wells, and the application of the QRA in pore pressure and fracture gradient prediction is researched repeatedly; some quantitative risk assessment methods for external extrusion resistance and internal pressure resistance of the casing are established based on structural reliability and a random theory so as to make up for the defects of the traditional safety coefficient method.
However, these QRA methods are aimed at the safety and reliability of the casing during oil and gas drilling operations, and the multi-stage sand fracturing is closely related to the drilling operations, so the evaluation results can only represent the static risk state after the casing is put into the formation compared to the fracturing operations. In the fracturing operation process of the shale oil and gas deep well ultra-deep well, on-line working condition monitoring parameters such as pumping pressure, discharge capacity, sand ratio and the like change along with time, which is the most real-time and most direct basis for field operators to acquire underground operation conditions and judge whether a casing pipe is invalid or not; in addition, numerical simulation verifies that the correlation relationship exists between the failure of the casing and high pressure, large discharge capacity, sand-containing erosion and continuous operation, so that the risk of the casing failure has dynamic real-time performance. In conclusion, the static QRA method cannot meet the requirements of real-time monitoring and dynamic evaluation of the failure risk of the casing in an operation field, and is difficult to ensure the safety and reliability of the service of the casing in the multi-section sand fracturing process.
Disclosure of Invention
The embodiment of the application aims to provide a method and a device for quantitatively evaluating the failure risk of a fracturing sleeve of a deep well and an ultra-deep well in real time, so that the failure risk of the sleeve is monitored and dynamically evaluated in real time.
In order to achieve the above object, in one aspect, the present application provides a method for quantitatively evaluating failure risk of a deep well and ultra-deep well fracturing casing in real time, including:
determining the probability distribution of the pressure load and the compressive strength of the casing;
determining the static failure probability of the casing according to the probability distribution;
acquiring online working condition monitoring parameters of the casing according to a fracturing construction curve and a fracturing construction record, determining a value function between the online working condition monitoring parameters and the casing failure probability, and determining the dynamic failure probability of the casing in the fracturing process according to the value function;
and constructing a risk evaluation matrix of the casing according to the static failure probability and the dynamic failure probability, and evaluating the failure risk of the casing according to the risk evaluation matrix.
The method for quantitatively evaluating the failure risk of the deep well and ultra-deep well fracturing casing in real time determines the probability distribution of the pressure load and the compressive strength of the casing, and comprises the following steps:
and determining the probability distribution of the external extrusion load, the internal pressure load, the external extrusion resistance strength and the internal pressure resistance strength of the casing based on a Monte Carlo random sampling method.
In the real-time quantitative evaluation method for the failure risk of the deep well and ultra-deep well fracturing casing, the probability distribution process of the external extrusion load of the casing is determined based on a Monte Carlo random sampling method, and the external extrusion load of the casing is determined according to the following formula:
in the formula, pceThe load is the external extrusion load of the sleeve; ecThe modulus of elasticity of the sleeve; v. ofcIs the cannula poisson's ratio; m is the ratio of the inner diameter to the outer diameter of the sleeve; esIs the formation elastic modulus; v. ofsIs the formation poisson's ratio; σ is uniformly stressed, andσmaxto maximize the ground stress, σminIs the minimum ground stress; ccThe cement sheath unloading coefficient; k is a radical ofmIs the hollowing coefficient; rho is the drilling fluid density; h is the calculated point depth for the casing.
In the real-time quantitative evaluation method for the failure risk of the deep well and ultra-deep well fracturing casing, the internal pressure load of the casing is determined according to the following formula in the process of determining the probability distribution of the internal pressure load of the casing based on the Monte Carlo random sampling method:
pbe2＝pp-0.00981ρwh
in the formula, pbe1The internal pressure load of the surface casing or the technical casing; p is a radical ofbe2Is the probability distribution of the internal pressure load of the production casing or the casing tail; rhomaxMaximum drilling fluid density; hsThe casing running depth or the casing shoe depth; h is the calculated point depth of the casing; rhogIs the relative density of oil gas; rhowIs the formation water density; p is a radical ofpIs the formation or hydrocarbon reservoir pressure load.
According to the method for real-time quantitative evaluation of the failure risk of the deep well and ultra-deep well fracturing casing, in the process of determining the probability distribution of the external extrusion resistance strength of the casing based on the Monte Carlo random sampling method, the external extrusion resistance strength of the casing is determined according to the following formula:
in the formula, pcaThe extrusion resistance of the sleeve; kpIs a coefficient of load non-uniformity, and Kp＝|(q1+q2)/q1|，q1For uniform loading, q2Is an elliptical load; k is the external-internal diameter ratio of the sleeve, and K is ro/ri，roIs the outer radius of the casing, riIs the inner radius of the sleeve; p is a radical of0API extrusion strength for the casing; ryIs the yield limit of the pipe.
In the real-time quantitative evaluation method for the failure risk of the deep well and ultra-deep well fracturing casing, the internal pressure resistance strength of the casing is determined according to the following formula in the process of determining the probability distribution of the internal pressure resistance strength of the casing based on the Monte Carlo random sampling method:
in the formula, pbaThe internal pressure resistance of the sleeve; p is a radical ofboThe value is a calibrated value of the internal pressure resistance; r isoThe outer radius of the sleeve; r isiIs the inner radius of the sleeve; p is a radical ofoIs the external liquid column pressure; sigmaaIs the axial stress; ryIs the yield limit of the pipe.
According to the method for quantitatively evaluating the failure risk of the deep well and ultra-deep well fracturing casing in real time, the static failure probability of the casing is determined according to the probability distribution, and the method comprises the following steps:
determining a static failure probability of the casing according to the following formula:
F＝1-R
in the formula (f)Z(Z) is a probability density function, Z is an interference random variable, and Z is Q-S,and obey normal distribution; s, Q are continuous random variables of the pressure load and the compressive strength of the casing respectively, which obey normal distribution and are independent of each other; sigmaZIs the standard deviation of the interfering random variables, anσS、σQRespectively is the standard deviation of the pressure load and the compressive strength of the sleeve; mu.sZIs the mean of the interfering random variables, and muZ＝μS-μQ；μS、μQRespectively the average values of the pressure load and the compressive strength of the sleeve; r, F static reliability and static failure probability of the casing respectively.
The method for evaluating the failure risk of the deep well and ultra-deep well fracturing casing in real time and quantitatively determines a value function between the online working condition monitoring parameter and the casing failure probability according to the following formula:
in the formula, X, Pi、CiAnd KiAre respectively a merit function viThe variable, the shape parameter, the inflection point abscissa and the inflection point ordinate; xmax，XminThe maximum value and the minimum value of X are respectively; b is a limiting cost function viIn the interval [0,1]An internally varying parameter.
According to the real-time quantitative evaluation method for the failure risk of the deep well and ultra-deep well fracturing casing, the dynamic failure probability of the casing in the fracturing process is determined according to the following formula:
determining the dynamic failure probability of the casing in the fracturing process;
in the formula，Is,iIs a preset first index; i isvIs the dynamic failure probability of the casing; v. ofiAs a function of value, λi、λs,iAre each vi、Is,iThe weight of (c); n is the index number.
According to the method for real-time quantitative evaluation of the failure risk of the deep well and ultra-deep well fracturing casing, according to the static failure probability and the dynamic failure probability, the construction of the risk evaluation matrix of the casing comprises the following steps:
hierarchically associating the static failure probability with the dynamic failure probability to construct a failure probability hierarchical matrix of the casing;
and associating the failure probability grading of the casing with a preset consequence severity grading to construct a risk assessment matrix of the casing.
On the other hand, the embodiment of this application provides a deep well ultra-deep well fracturing sleeve pipe failure risk real-time quantitative evaluation device, includes:
the probability distribution determining module is used for determining the probability distribution of the pressure load and the compressive strength of the casing;
a first probability determination module for determining a probability of static failure of the casing from the probability distribution;
the second probability determination module is used for acquiring online working condition monitoring parameters of the casing according to a fracturing construction curve and a fracturing construction record, determining a value function between the online working condition monitoring parameters and the casing failure probability, and determining the dynamic failure probability of the casing in the fracturing process according to the value function;
and the failure risk evaluation module is used for constructing a risk evaluation matrix of the casing according to the static failure probability and the dynamic failure probability and evaluating the failure risk of the casing according to the risk evaluation matrix.
In another aspect, an embodiment of the present application provides a computer storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the following steps:
determining the probability distribution of the pressure load and the compressive strength of the casing;
determining the static failure probability of the casing according to the probability distribution;
acquiring online working condition monitoring parameters of the casing according to a fracturing construction curve and a fracturing construction record, determining a value function between the online working condition monitoring parameters and the casing failure probability, and determining the dynamic failure probability of the casing in the fracturing process according to the value function;
and constructing a risk evaluation matrix of the casing according to the static failure probability and the dynamic failure probability, and evaluating the failure risk of the casing according to the risk evaluation matrix.
According to the technical scheme provided by the embodiment of the application, the probability distribution of the pressure load and the compressive strength of the casing is determined, and the static failure probability of the casing is determined according to the probability distribution; then, acquiring online working condition monitoring parameters of the casing according to the fracturing construction curve and the fracturing construction record, determining a value function between the online working condition monitoring parameters and the casing failure probability, and determining the dynamic failure probability of the casing in the fracturing process according to the value function; and finally, according to the static failure probability and the dynamic failure probability, constructing a risk evaluation matrix of the casing, and evaluating the failure risk of the casing according to the risk evaluation matrix, so that the real-time quantitative evaluation of the failure risk of the casing of the deep well ultra-deep well fracturing is realized, the requirements of the operation site on the real-time monitoring and the dynamic evaluation of the casing failure risk are met, and the safety and the reliability of the casing during the multi-stage sand fracturing are improved.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, it is obvious that the drawings in the following description are only some embodiments described in the present application, and for those skilled in the art, other drawings can be obtained according to the drawings without any creative effort. In the drawings:
fig. 1 is a flowchart of a method for real-time quantitative evaluation of the failure risk of a deep well and ultra-deep well fracturing casing in one embodiment of the present application;
FIGS. 2a-2d are schematic diagrams illustrating probability distributions of external compressive load, internal compressive load, external compressive strength, and internal compressive strength of a bushing according to an embodiment of the present disclosure;
FIG. 3a is a fracture profile in an embodiment of the present application;
FIG. 3b is a graph of the dynamic failure probability of a production casing in an embodiment of the present application;
FIG. 4a is a visual risk map of the failure probability of a production casing in an embodiment of the present application;
FIG. 4b is a visual risk map of the risk of failure of a production casing in another embodiment of the present application;
fig. 5 is a structural block diagram of a deep well ultra-deep well fracturing casing failure risk real-time quantitative evaluation device in an embodiment of the present application.
Detailed Description
In order to make those skilled in the art better understand the technical solutions in the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
Referring to fig. 1, the method for real-time quantitative evaluation of the failure risk of a deep well and ultra-deep well fracturing casing according to the embodiment of the present application may include the following steps:
and S101, determining the probability distribution of the pressure load and the compressive strength of the casing.
In the embodiment of the application, the probability distribution of the external extrusion load, the internal pressure load, the external extrusion resistance strength and the internal pressure resistance strength of the casing can be determined by adopting a Monte-Carlo random sampling method. Wherein:
in the process of determining the probability distribution of the external extrusion load of the casing based on the Monte Carlo random sampling method, the external extrusion load of the casing is determined according to the following formula:
in the formula, pceThe load is the external extrusion load of the sleeve; ecThe modulus of elasticity of the sleeve; v. ofcIs the cannula poisson's ratio; m is the ratio of the inner diameter to the outer diameter of the sleeve; esIs the formation elastic modulus; v. ofsIs the formation poisson's ratio; σ is uniformly stressed, andσmaxto maximize the ground stress, σminIs the minimum ground stress; ccThe cement sheath unloading coefficient; k is a radical ofmIs the hollowing coefficient; rho is the drilling fluid density; h is the calculated point depth for the casing.
In the process of determining the probability distribution of the internal pressure load of the casing based on the Monte Carlo random sampling method, the internal pressure load of the casing is determined according to the following formula:
pbe2＝pp-0.00981ρwh
in the formula, pbe1The internal pressure load of the surface casing or the technical casing; p is a radical ofbe2Is the probability distribution of the internal pressure load of the production casing or the casing tail; rhomaxMaximum drilling fluid density; hsThe casing running depth or the casing shoe depth; h is the calculated point depth of the casing; rhogIs the relative density of oil gas; rhowIs the formation water density; p is a radical ofpIs the formation or hydrocarbon reservoir pressure load.
In the process of determining the probability distribution of the external extrusion resistance strength of the casing based on the Monte Carlo random sampling method, the external extrusion resistance strength of the casing is determined according to the following formula:
in the formula, pcaThe extrusion resistance of the sleeve; kpIs a coefficient of load non-uniformity, and Kp＝|(q1+q2)/q1|，q1For uniform loading, q2Is an elliptical load; k is the external-internal diameter ratio of the sleeve, and K is ro/ri，roIs the outer radius of the casing, riIs the inner radius of the sleeve; p is a radical of0API extrusion strength for the casing; ryIs the yield limit of the pipe.
In the process of determining the probability distribution of the internal pressure resistance strength of the casing based on the Monte Carlo random sampling method, the internal pressure resistance strength of the casing is determined according to the following formula:
in the formula, pbaThe internal pressure resistance of the sleeve; p is a radical ofboThe value is a calibrated value of the internal pressure resistance; r isoThe outer radius of the sleeve; r isiIs the inner radius of the sleeve; p is a radical ofoIs the external liquid column pressure; sigmaaIs the axial stress; ryIs the yield limit of the pipe.
S102, determining the static failure probability of the casing according to the probability distribution.
The static failure probability is the static failure probability of the shale gas deep well ultra-deep well fracturing operation starting and the casing running into the stratum. Determining a static failure probability of the casing according to the probability distribution, comprising:
determining a static failure probability of the casing according to the following formula:
F＝1-R
in the formula (f)Z(Z) isA probability density function, wherein Z is an interference random variable and is Q-S and obeys normal distribution; s, Q are continuous random variables of the pressure load and the compressive strength of the casing respectively, which obey normal distribution and are independent of each other; sigmaZIs the standard deviation of the interfering random variables, anσS、σQRespectively is the standard deviation of the pressure load and the compressive strength of the sleeve; mu.sZIs the mean of the interfering random variables, and muZ＝μS-μQ；μS、μQRespectively the average values of the pressure load and the compressive strength of the sleeve; r, F static reliability and static failure probability of the casing respectively.
S103, acquiring online working condition monitoring parameters of the casing according to the fracturing construction curve and the fracturing construction record, determining a value function between the online working condition monitoring parameters and the casing failure probability, and determining the dynamic failure probability of the casing in the fracturing process according to the value function.
In the embodiment of the application, an evaluation index system of the casing failure probability in the shale gas deep well ultra-deep well fracturing operation process can be established according to typical casing failure modes, namely fatigue cracks, abrasive wear and corrosion defects, and is shown in table 1. Because the casing pipe is subjected to different pump pressures, discharge capacities and sand ratios in continuous operation, some working condition monitoring parameters with time accumulation effect, such as stage sand amount, accumulated liquid amount and the like, can be considered in an important way. Meanwhile, a value function model between the online working condition monitoring parameters and the casing failure probability can be established, and the values of the model parameters are determined, which is shown in table 1.
Thus, the determining the dynamic failure probability of the casing may comprise the steps of:
firstly, acquiring online working condition monitoring parameters of a casing through a fracturing construction curve and a fracturing construction record;
secondly, according to the following formula:
determining a cost function between the online working condition monitoring parameters and the failure probability of the casing;
then, according to the formulaAnd determining the dynamic failure probability of the casing in the fracturing process.
In the formula, X, Pi、CiAnd KiAre respectively a merit function viThe variable, the shape parameter, the inflection point abscissa and the inflection point ordinate; xmax，XminThe maximum value and the minimum value of X are respectively; b is a limiting cost function viIn the interval [0,1]An internally varying parameter; i iss,iA preset first index (for example, a first-level index shown in table 1); i isvIs the dynamic failure probability of the casing; lambda [ alpha ]i、λs,iAre each vi、Is,iThe weight of (c); n is the index number.
TABLE 1 comprehensive evaluation index system for casing and parameter value of value function
S104, constructing a risk evaluation matrix of the casing according to the static failure probability and the dynamic failure probability, and evaluating the failure risk of the casing according to the risk evaluation matrix.
Specifically, first, the static failure probability and the dynamic failure probability are hierarchically associated to construct a failure probability hierarchical matrix of the casing (as shown in table 2); and then, associating the failure probability grading of the casing with a preset consequence severity grading, and constructing a risk evaluation matrix (shown in a table 3) of the casing, so that the failure risk of the casing can be evaluated according to the risk evaluation matrix to reveal the safe reliability state and the risk grade area of the casing, and whether the fracturing construction operation can be continued is judged. For example, the static failure probability is 0.05, and is in class IV; the dynamic failure probability is 0.3, and the level II is achieved; according to table 2, the failure probability of the casing is in level 3, and the severity of the corresponding consequence is in level 3; the risk of failure is at level C2, belonging to the middle zone, according to table 3. The necessary risk diversion, reduction or elimination should be undertaken at this point, under the least Reasonably feasible (As Low As accessible practical) guidelines, but the existing risk level can be maintained if the cost of reducing risk outweighs the benefit of improvement.
TABLE 2 hierarchical matrix of casing failure probabilities
TABLE 3 Risk assessment matrix
ALARP principle: provide that the a-level is of negligible risk. ② define B, C1 ratings as belonging to widely accepted areas where no further risk reduction measures need to be taken. ③ the C2 and D1 classes are assigned to the middle zone, i.e. the tolerant zone or the ALARP zone, where measures are preferably taken to reduce the risk, but no action may be taken if the cost and profit proportions are unbalanced. Fourth, the D2, E rating is specified to be in an unacceptable area where risks are intolerable except in special cases where measures to reduce risks must be taken.
Of course, in the embodiment of the present application, the treatment process may be performed simultaneously with operations such as fracturing, and has a real-time effect.
According to the embodiment of the application, the static failure probability, the dynamic failure probability and the consequence severity are integrated, quantitative evaluation and grading evaluation research is carried out on the casing failure risk by constructing a risk matrix, and the characteristics of shale gas ultra-deep well fracturing operation such as high pressure, large discharge capacity and sand erosion are comprehensively considered, so that the accuracy of an evaluation result is remarkably improved, and the requirements of real-time monitoring of an operation site and accurate mastering of the casing failure risk are favorably met.
To facilitate an understanding of the present application, an exemplary embodiment of the present application is described below:
through on-site investigation of a certain well, the well is found to belong to an ultra-deep pre-exploration well, wherein the slant depth is 5700m, the vertical depth is 4417.43m, and the horizontal section is 1034.23 m. Taking a production casing (steel grade TP125TS, outer diameter 177.8mm, wall thickness 12.65mm and section length 5696.77m) as an example, failure probability solving, quantitative risk assessment and risk grading assessment research are carried out on the production casing according to the steps.
Firstly, the Monte-Carlo random sampling method is used to determine the probability distribution rules of the external extrusion load, the internal pressure load, the external extrusion strength and the internal pressure strength of the well production casing respectively (see the step S101 for the specific method). Through 100000 calculation simulations, it was found that the load and strength of the material obey normal distribution, as shown in fig. 2a-2 d. The abscissa in fig. 2a to 2d is the external extrusion load, the internal pressure load, the external extrusion strength and the internal pressure strength, respectively; in FIGS. 2a to 2d, the ordinate represents probability density, σ represents standard deviation, and μ represents mean.
Next, the static failure probability of the casing is determined (see step S102 above for a specific method). The reliability of the well production casing against external extrusion and internal pressure is found to be 1.00 through calculation, namely the static failure probability is 0.
Then, the dynamic failure probability of the casing is determined (see step S103 above for a specific method). On-line working condition monitoring parameters, such as pumping pressure, discharge capacity, sand ratio, stage sand amount, accumulated liquid amount and the like, are obtained through a fracturing construction curve (shown in figure 3 a) of a certain section of the well and a fracturing construction record (shown in table 4). In fig. 3a, the left-hand outer layer ordinate is displacement, the left-hand inner layer ordinate is pump pressure, and the right-hand ordinate is sand ratio. And calculating to obtain the dynamic failure probability of the production casing in the section of fracturing construction process, as shown in fig. 3 b.
TABLE 4 fracturing construction record (partial intercept)
Finally, a risk assessment matrix of the casing is constructed according to the static failure probability and the dynamic failure probability, and the failure risk of the casing is assessed according to the risk assessment matrix (see step S104 above for a specific method).
Because the static failure probability of the well production casing is 0 and belongs to the level I, the dynamic failure probability is mostly in the level II. According to the casing failure probability hierarchical matrix and static and dynamic integrated solution shown in table 2, the overall failure probability of the production casing in the section of fracturing construction process is comprehensively analyzed and mostly in 2 levels, as shown in fig. 4 a.
In addition to the failure probability, the severity of the consequences of the failure risk of the production casing needs to be taken into account; the consequence severity contains personnel, equipment, environment, and reputation according to the risk assessment matrix shown in table 3. Referring to the on-site well history data, it is specified here that: from 8:47:30 of fracturing to 11:34:11 of fracturing, personnel are not casualty, equipment is not damaged, the environment is slightly influenced, reputation is not influenced, and the severity of consequences belongs to level I; from 11:34:11 in fracturing to 13:34:00 at the end of fracturing, personnel are not casualty, equipment is slightly damaged, the environment is locally influenced, the reputation is not influenced, and the severity of the consequences belongs to the level II.
Accordingly, the safe and reliable state of the well production casing can be comprehensively measured, namely the failure risk of the well production casing is in A grade or B grade in the section of fracturing construction process, as shown in FIG. 4B. According to the ALARP principle, the risk is negligible or belongs to a wide acceptable area, and the fracturing construction operation can be continued.
Referring to fig. 5, the device for real-time quantitative evaluation of the failure risk of a deep well and ultra-deep well fracturing casing according to the embodiment of the present application may include:
a probability distribution determination module 51, which may be configured to determine a probability distribution of the pressure load and the compressive strength of the casing;
a first probability determination module 52 operable to determine a probability of static failure of the casing from the probability distribution;
the second probability determination module 53 may be configured to obtain an online condition monitoring parameter of the casing according to the fracturing construction curve and the fracturing construction record, determine a value function between the online condition monitoring parameter and the casing failure probability, and determine the dynamic failure probability of the casing in the fracturing process according to the value function;
the failure risk evaluation module 54 may be configured to construct a risk evaluation matrix of the casing according to the static failure probability and the dynamic failure probability, and evaluate the failure risk of the casing according to the risk evaluation matrix.
The apparatus of the embodiment of the present application corresponds to the method of the embodiment, and therefore, for details of the apparatus of the present application, please refer to the method of the embodiment, which is not described herein again.
For convenience of description, the above devices are described as being divided into various units by function, and are described separately. Of course, the functionality of the units may be implemented in one or more software and/or hardware when implementing the present application.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The application may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The application may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the system embodiment, since it is substantially similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment.
The above description is only an example of the present application and is not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.
Claims (11)
1. A real-time quantitative evaluation method for failure risk of a deep well and ultra-deep well fracturing sleeve is characterized by comprising the following steps:
determining the probability distribution of the pressure load and the compressive strength of the casing;
determining the static failure probability of the casing according to the probability distribution;
acquiring online working condition monitoring parameters of the casing according to a fracturing construction curve and a fracturing construction record, determining a value function between the online working condition monitoring parameters and the casing failure probability, and determining the dynamic failure probability of the casing in the fracturing process according to the value function;
constructing a risk evaluation matrix of the casing according to the static failure probability and the dynamic failure probability, and evaluating the failure risk of the casing according to the risk evaluation matrix;
the step of constructing a risk assessment matrix of the casing according to the static failure probability and the dynamic failure probability comprises the following steps:
hierarchically associating the static failure probability with the dynamic failure probability to construct a failure probability hierarchical matrix of the casing;
and associating the failure probability grading of the casing with a preset consequence severity grading to construct a risk assessment matrix of the casing.
2. The method for real-time quantitative evaluation of the failure risk of the deep well and ultra-deep well fracturing casing according to claim 1, wherein the determining of the probability distribution of the pressure load and the compressive strength of the casing comprises:
and determining the probability distribution of the external extrusion load, the internal pressure load, the external extrusion resistance strength and the internal pressure resistance strength of the casing based on a Monte Carlo random sampling method.
3. The method for real-time quantitative evaluation of the failure risk of the deep well and ultra-deep well fracturing casing according to claim 2, wherein in the process of determining the probability distribution of the external extrusion load of the casing based on the monte carlo random sampling method, the external extrusion load of the casing is determined according to the following formula:
in the formula, pceThe load is the external extrusion load of the sleeve; ecThe modulus of elasticity of the sleeve; v. ofcIs the cannula poisson's ratio; m is the ratio of the inner diameter to the outer diameter of the sleeve; esIs the formation elastic modulus; v. ofsIs the formation poisson's ratio; σ is uniformly stressed, and，σmaxto maximize the ground stress, σminIs the minimum ground stress; ccThe cement sheath unloading coefficient; k is a radical ofmIs the hollowing coefficient; rho is the drilling fluid density; h is the calculated point depth for the casing.
4. The method for real-time quantitative evaluation of the failure risk of the deep well and ultra-deep well fracturing casing according to claim 2, wherein in the process of determining the probability distribution of the internal pressure load of the casing based on the monte carlo random sampling method, the internal pressure load of the casing is determined according to the following formula:
pbe2＝pp-0.00981ρwh
in the formula, pbe1The internal pressure load of the surface casing or the technical casing; p is a radical ofbe2Is the probability distribution of the internal pressure load of the production casing or the casing tail; rhomaxMaximum drilling fluid density; hsFor the lower depth of the casingOr shoe depth; h is the calculated point depth of the casing; rhogIs the relative density of oil gas; rhowIs the formation water density; p is a radical ofpIs the formation or hydrocarbon reservoir pressure load.
5. The method for real-time quantitative evaluation of the failure risk of the deep well and ultra-deep well fracturing casing according to claim 2, wherein in the process of determining the probability distribution of the external extrusion resistance strength of the casing based on the monte carlo random sampling method, the external extrusion resistance strength of the casing is determined according to the following formula:
in the formula, pcaThe extrusion resistance of the sleeve; kpIs a coefficient of load non-uniformity, and Kp＝|(q1+q2)/q1|，q1For uniform loading, q2Is an elliptical load; k is the external-internal diameter ratio of the sleeve, and K is ro/ri，roIs the outer radius of the casing, riIs the inner radius of the sleeve; p is a radical of0API extrusion strength for the casing; ryIs the yield limit of the pipe.
6. The method for real-time quantitative evaluation of the failure risk of the deep well and ultra-deep well fracturing casing according to claim 2, wherein in the process of determining the probability distribution of the internal pressure resistance strength of the casing based on the monte carlo random sampling method, the internal pressure resistance strength of the casing is determined according to the following formula:
in the formula, pbaThe internal pressure resistance of the sleeve; p is a radical ofboThe value is a calibrated value of the internal pressure resistance; r isoThe outer radius of the sleeve; r isiIs the inner radius of the sleeve; p is a radical ofoIs the external liquid column pressure; sigmaaIs the axial stress; ryIs the yield limit of the pipe.
7. The method for real-time quantitative evaluation of the failure risk of a deep well and ultra-deep well fracturing casing according to claim 2, wherein the determining the static failure probability of the casing according to the probability distribution comprises:
determining a static failure probability of the casing according to the following formula:
F＝1-R
wherein f (Z) is a probability density function, Z is an interference random variable, and Z is Q-S and follows a normal distribution; s, Q are continuous random variables of the pressure load and the compressive strength of the casing respectively, which obey normal distribution and are independent of each other; sigmaZIs the standard deviation of the interfering random variables, anσS、σQRespectively is the standard deviation of the pressure load and the compressive strength of the sleeve; mu.sZIs the mean of the interfering random variables, and muZ＝μS-μQ；μS、μQRespectively the average values of the pressure load and the compressive strength of the sleeve; r, F static reliability and static failure probability of the casing respectively.
8. The method for real-time quantitative evaluation of the failure risk of the deep well and ultra-deep well fracturing casing according to claim 1, wherein a value function between the online working condition monitoring parameter and the casing failure probability is determined according to the following formula:
in the formula, X,Pi、CiAnd KiAre respectively a merit function viThe variable, the shape parameter, the inflection point abscissa and the inflection point ordinate; xmax，XminThe maximum value and the minimum value of X are respectively; b is a limiting cost function viIn the interval [0,1]An internally varying parameter.
9. The method for real-time quantitative evaluation of the failure risk of the deep well and ultra-deep well fracturing casing according to claim 1, wherein the dynamic failure probability of the casing in the fracturing process is determined according to the following formula:
determining the dynamic failure probability of the casing in the fracturing process;
in the formula Is,iIs a preset first index; i isvIs the dynamic failure probability of the casing; v. ofiAs a function of value, λi、λs,iAre each vi、Is,iThe weight of (c); n is the index number.
10. The utility model provides a deep well ultra-deep well fracturing sleeve pipe inefficacy risk real-time quantitative evaluation device which characterized in that includes:
the probability distribution determining module is used for determining the probability distribution of the pressure load and the compressive strength of the casing;
a first probability determination module for determining a probability of static failure of the casing from the probability distribution;
the second probability determination module is used for acquiring online working condition monitoring parameters of the casing according to a fracturing construction curve and a fracturing construction record, determining a value function between the online working condition monitoring parameters and the casing failure probability, and determining the dynamic failure probability of the casing in the fracturing process according to the value function;
the failure risk evaluation module is used for constructing a risk evaluation matrix of the casing according to the static failure probability and the dynamic failure probability and evaluating the failure risk of the casing according to the risk evaluation matrix;
the step of constructing a risk assessment matrix of the casing according to the static failure probability and the dynamic failure probability comprises the following steps:
hierarchically associating the static failure probability with the dynamic failure probability to construct a failure probability hierarchical matrix of the casing;
and associating the failure probability grading of the casing with a preset consequence severity grading to construct a risk assessment matrix of the casing.
11. A computer storage medium having a computer program stored thereon, the computer program, when executed by a processor, performing the steps of:
determining the probability distribution of the pressure load and the compressive strength of the casing;
determining the static failure probability of the casing according to the probability distribution;
acquiring online working condition monitoring parameters of the casing according to a fracturing construction curve and a fracturing construction record, determining a value function between the online working condition monitoring parameters and the casing failure probability, and determining the dynamic failure probability of the casing in the fracturing process according to the value function;
constructing a risk evaluation matrix of the casing according to the static failure probability and the dynamic failure probability, and evaluating the failure risk of the casing according to the risk evaluation matrix;
the step of constructing a risk assessment matrix of the casing according to the static failure probability and the dynamic failure probability comprises the following steps:
hierarchically associating the static failure probability with the dynamic failure probability to construct a failure probability hierarchical matrix of the casing;
and associating the failure probability grading of the casing with a preset consequence severity grading to construct a risk assessment matrix of the casing.
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| CN111563309B (en) * | 2019-01-29 | 2022-11-01 | 中国石油天然气股份有限公司 | Method and device for determining tensile safety factor of casing of ultra-deep well |
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| CN111709125B (en) * | 2020-05-26 | 2023-01-24 | 重庆科技学院 | Method for evaluating safety reliability of ultra-deep well casing based on uncertainty of outer extrusion load |
| CN114059991A (en) * | 2020-08-05 | 2022-02-18 | 中石化石油工程技术服务有限公司 | Deep well complex stratum well body structure design method based on multi-objective optimization |
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| CN113431496A (en) * | 2021-05-31 | 2021-09-24 | 中国舰船研究设计中心 | Drilling and production ship cooperative operation fault diagnosis and decision-making assisting method |
| CN113513286B (en) * | 2021-07-19 | 2022-03-01 | 重庆科技学院 | System for evaluating extrusion failure of production casing pipe during packer deblocking |
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