Data and mechanism model combined driven gas cut monitoring method

Abstract

The invention discloses a data and mechanism model combined driven gas cut monitoring method. The method comprises the following steps: 1) acquiring basic input parameters required by a simulation well; (2) establishing a single-phase flow mechanical model of the shaft under the normal working condition on the basis of a drilling construction process; (3) an outlet flow correction factor and an annular pressure loss correction factor are introduced into the normal working condition single-phase flow mechanical model, and a gas cut working condition monitoring model is constructed; 4) acquiring pressure data and flow data, and estimating an outlet flow correction factor and an annular pressure loss correction factor through an unscented Kalman filtering algorithm; 5) calculating a decision function of an outlet flow correction factor and an annular pressure loss correction factor through a generalized likelihood ratio test algorithm; and 6) comparing the decision function value with a preset threshold value to judge whether gas cut may occur, and if so, sending out a gas cut early warning signal. The method is beneficial to early monitoring of complex working conditions, and meets the requirements for early warning and monitoring of gas cut overflow of deep wells, ultra-deep wells and complex stratums.

Description

Technical Field

[0001] The present invention relates to the technical field of oil and gas exploitation, and in particular to a gas intrusion monitoring method jointly driven by data and mechanism models. Background Art

[0002] Drilling is a complex underground project with strong uncertainty and concealment. It is easy to induce gas invasion when drilling in deep, complex and narrow safety density window formations. Gas invasion is caused by the effective pressure of the annulus at the bottom of the well being being less than the pore pressure of the formation, which causes the formation gas to invade the wellbore. During the process of gas invading the wellbore and migrating upward along the annulus, the gas pressure is continuously released, the volume gradually expands, and the speed becomes faster and faster. Especially when the gas is close to the ground, its volume expansion rate is very fast. If effective control measures are not taken in time, it is likely to cause a blowout accident. Therefore, good gas invasion monitoring and early warning during drilling is the key to well control safety.

[0003] The earlier the gas intrusion is discovered, the easier it is to reestablish the bottom hole pressure balance. According to different monitoring principles, the gas intrusion monitoring methods mainly include: wave velocity method, flow rate and flow difference method, comprehensive measurement parameter method, artificial intelligence method, and intelligent drill pipe method. Usually in the early stage of gas intrusion, the sensitive parameters change very little. The above methods directly use the amplitude changes of sensitive parameters to monitor gas intrusion, which has the disadvantage of time lag in monitoring. It can be seen that although the existing gas intrusion monitoring system can meet the alarm requirements, the gas intrusion identification time lag and high false alarm rate do not meet the gas intrusion warning requirements. Summary of the invention

[0004] In view of the shortcomings of the above-mentioned prior art, the present invention proposes a gas invasion monitoring method jointly driven by data and mechanism models. Based on the measurement data and the wellbore hydraulic model during the pressure controlled drilling process, the unscented Kalman filter algorithm is used to invert the model parameters, which amplifies the change rate of sensitive parameters caused by gas invasion, and can further improve the real-time performance of downhole overflow monitoring.

[0005] The technical solution adopted by the present invention comprises the following steps:

[0006] Step 1: Obtain the basic parameters required for the simulated well;

[0007] Step 2: Based on the drilling construction technology, a single-phase flow hydraulic model of the wellbore under normal working conditions is constructed;

[0008] Step 3: Based on the single-phase flow hydraulics model of normal wellbore conditions, the outlet flow correction factor and the annular pressure loss correction factor are introduced to construct a gas invasion condition monitoring model;

[0009] Step 4: Collect pressure data and flow data, and estimate the outlet flow correction factor and annular pressure loss correction factor by using the unscented Kalman filter algorithm;

[0010] Step 5: construct the decision functions of the orifice flow correction factor and the annular pressure loss correction factor respectively through the generalized likelihood ratio test algorithm;

[0011] Step 6: By comparing the decision function value with the preset threshold, determine whether gas intrusion is likely to occur. If so, issue a gas intrusion warning.

[0012] Furthermore, the basic parameters described in step 1 include: well depth, sounding, well inclination, azimuth, well diameter, drill pipe inner diameter, drill pipe outer diameter, drill bit nozzle area, circulation displacement, drilling fluid density, formation density, drilling fluid plastic viscosity, drilling fluid specific heat, formation specific heat, inlet temperature, outlet temperature, geothermal gradient, formation thermal conductivity, drilling fluid thermal conductivity, and drill pipe thermal conductivity.

[0013] Furthermore, the specific steps of step 2 include:

[0014] Step 201: defining the assumptions of the single-phase flow hydraulic model under normal wellbore conditions;

[0015] Step 202: Based on the assumptions, establish the control equations of the single-phase flow hydraulic model under normal wellbore conditions;

[0016] Step 203: establishing auxiliary equations of a single-phase flow hydraulic model under normal wellbore conditions;

[0017] Step 204: Establish a flow characteristic equation for the flow through the wellhead throttle valve of the managed pressure drilling:

[0018]

[0019] Among them, q c represents the volume flow through the throttle valve, u c Indicates the throttle valve opening, k(u c ) represents the flow coefficient characteristic function of the throttle valve, ρ0 represents the density of clean water at 16°C, △p0 represents the pressure drop across the throttle valve when the opening is equal to 100%, and p c Indicates the pressure upstream of the throttle valve.

[0020] Furthermore, the assumptions defined in step 201 include:

[0021] 1) Only one-dimensional flow along the flow direction in the drill string and annulus is considered;

[0022] 2) The thermophysical parameters of the drilling fluid are constant. The density ρ and viscosity μ of the drilling fluid depend on the pressure and temperature, which can be expressed as ρ = ρ(p, T) and μ = μ(p, T), where p represents pressure and T represents temperature;

[0023] 3) The density, specific heat and thermal conductivity of the formation are constant;

[0024] 4) The concentration of cuttings in the drilling fluid is negligible and the average wellbore diameter is taken.

[0025] Furthermore, the control equation established in step 202 includes:

[0026] 1) Drilling fluid continuity equation in drill string

[0027]

[0028] Among them, ρ l represents the fluid density, A represents the flow channel area, v l represents the fluid velocity; represents the deviator in the axial direction of the wellbore, represents the partial derivative of time; the subscript in represents the fluid in the column;

[0029] 2) Continuity equation of drilling fluid in annulus

[0030]

[0031] Where a represents the annulus fluid;

[0032] 3) Drilling fluid momentum conservation equation in the drill string

[0033]

[0034] Among them, p f represents pressure loss, θ represents well inclination, and g represents gravitational acceleration constant;

[0035] 4) Drilling fluid momentum conservation equation in the annulus

[0036]

[0037] 5) Drilling fluid energy conservation equation in drill string

[0038]

[0039] Among them, Q p represents the viscous heat source term of the drilling fluid in the drill string, q l Represents the volume flow rate of the fluid, C l represents the specific heat capacity of the drilling fluid in the drill string, T p Indicates the temperature of the drilling fluid in the drill string, T w represents the temperature of the drill string, r pi Indicates the inner diameter of the drill string, h pi Indicates the heat transfer coefficient of the inner wall of the drill string;

[0040] 6) Drill pipe wall energy conservation equation

[0041]

[0042] Among them, λ w represents the thermal conductivity of the drill string, ρ w Indicates the density of the drill string, C w is the specific heat of the drill string, T a represents the temperature of the annular fluid, r po Indicates the outer diameter of the drill string, h po Indicates the heat transfer coefficient of the outer wall of the drill string;

[0043] 7) Annular space energy conservation equation

[0044]

[0045] Among them, Q a represents the viscous heat source term of the annular fluid, T c represents the temperature of the well wall, r ci represents the annulus radius, h ci It represents the heat transfer coefficient between the annulus and the wellbore wall;

[0046] 8) Wellbore energy conservation equation

[0047]

[0048] Among them, λ c represents the thermal conductivity of the wellbore wall, ρ c Indicates the density of the wellbore wall, C c represents the specific heat of the wellbore wall, r co Represents the outer diameter of the wellbore unit;

[0049] 9) Energy conservation equation in formations

[0050]

[0051] Among them, T f represents the temperature of the formation, r represents the radial radius of the formation area from the wellbore axis, ρ f Indicates the density of the formation, C f represents the specific heat of the formation, λ f Indicates the thermal conductivity of the formation;

[0052] 10) Annulus outlet volume flow equation

[0053] q c =(Av l ) c

[0054] Among them, q c is the annulus outlet volume flow rate, and the subscript c represents the annulus outlet position.

[0055] Furthermore, the auxiliary equation in step 203 includes:

[0056] 1) Forced convection heat transfer coefficient calculation equation

[0057]

[0058] Nu=0.023Re 0.8 P r 1 / 3

[0059]

[0060] Where Nu is the Nusselt number, P r represents the Prandtl number, Re represents the Reynolds number, λ l Indicates the thermal conductivity of drilling fluid, μ l Indicates the viscosity of the drilling fluid, D pi / po / ci Respectively represent the inner diameter of the drill string, the outer diameter of the drill string and the annulus diameter, D hy Indicates the hydraulic diameter, D in the drill string hy =D pi , in the annulus D hy =D ci -D po ;

[0061] 2) Calculation equation of heat source term generated by fluid flow friction

[0062]

[0063] Where F is the friction coefficient, D hv represents the hydraulic diameter, p f Indicates the frictional pressure loss per unit length;

[0064] The friction coefficient F under laminar flow is:

[0065] F=16 / R e

[0066] The friction coefficient F under laminar turbulent flow is:

[0067]

[0068] Among them, n g Represents the generalized flow index of the fluid.

[0069] Furthermore, the specific steps of step 3 include:

[0070] Step 301: Define the response characteristic parameters of the gas intrusion condition monitoring model:

[0071]

[0072] in, represents the annular pressure change rate, represents the annular pressure loss change rate, It represents the rate of change of annulus outlet flow rate; Indicates the measured data of annular space pressure under gas invasion conditions. Indicates the measured data of annular space pressure under normal working conditions. Indicates the calculated data of annular pressure loss under normal working conditions. It represents the measured data of the annulus outlet flow rate under gas invasion conditions. Indicates the measurement data of the annulus outlet flow rate under normal working conditions;

[0073] Step 302: Based on the response characteristic parameters, define the annulus pressure loss correction factor and the annulus outlet flow correction factor:

[0074]

[0075] Among them, θ a represents the annular pressure loss correction factor, θ q represents the annulus outlet flow correction factor;

[0076] Step 303: Set θ a and θ q They are introduced into the drilling fluid momentum conservation equation in the annulus and the annulus outlet volume flow equation respectively, thus establishing a gas invasion condition monitoring model:

[0077] q c =θ q (Av l ) c

[0078]

[0079] Furthermore, the specific steps of step 4 include:

[0080] Step 401: Obtain pressure data and flow data measurable by managed pressure drilling through a logging system arranged at the wellhead and a downhole PWD measurement tool;

[0081] Step 402: Construct the state equation and measurement equation for managed pressure drilling:

[0082] 1) Equation of state

[0083] θ(k)=θ(k-1)+w(k)

[0084] Where θ(k)=[θ a (k),θ q (k)], w(k)=[w1(k),w2(k)]∈R 2 represents process noise, k represents discrete time, a represents annular fluid, and q represents fluid flow rate;

[0085] 2) Measurement equation

[0086] y(k)=F d (θ(k-1),u(k))+v k

[0087] Among them, F d (·) represents the finite difference solution form of the gas intrusion monitoring model; v k ∈R 4 represents the measurement noise; u(k)=[q p (k),p c (k),q b (k)] represents the input vector, q p Indicates the standpipe pumping capacity, p c Indicates the pressure upstream of the throttle valve, q b represents the displacement of the back pressure pump; y(k) represents the observed variable, and the observed variable is the annulus bottom hole pressure p a , string bottom hole pressure p d , Standpipe pressure p s and the displacement downstream of the throttle valve q c ;

[0088] Step 403: Based on the measurement data obtained in step 401, the unscented Kalman filter algorithm is used to infer θ based on the gas intrusion condition monitoring model. a and θ q .

[0089] Furthermore, the steps of constructing the decision function in step 5 include:

[0090] Step 501: Based on the generalized likelihood ratio test algorithm, define two mutually exclusive propositions:

[0091] 1) Normal operating condition assumption H0

[0092] H0:z~p(μ0;H0)

[0093] 2) Gas intrusion condition assumption H1

[0094] H1:z~p(μ1;H1)

[0095] Among them, p(μ i ;H i ) is the probability density function of variable z under the specified hypothesis, μ i is the sequence mean parameter of variable z under the specified assumption;

[0096] Step 502: If the change occurs at time j, the sum of the log-likelihood ratios is:

[0097]

[0098] in, represents the sum of log-likelihood ratios;

[0099] Step 503: Substitute the maximum likelihood estimates of j and μ1 into the above formula to obtain the generalized likelihood test decision function:

[0100]

[0101] Step 504: For a Gaussian distribution random sequence with a data window length of N, establish the following decision function:

[0102]

[0103] Furthermore, the specific steps of step 6 include:

[0104] Step 601: Define the following inspection rules:

[0105] g(k)≤h, accept H0

[0106] g(k)>h, reject H0

[0107] Among them, h represents the judgment threshold, g(k) represents the decision function;

[0108] The relationship between the threshold h and the significance level α is expressed by the following formula:

[0109] p{g(k)>h|H0}≤α;

[0110] Step 602: Count the probability distribution of the Gaussian distribution random sequence before the known change occurs, pre-set the significance level α, and determine h through the above formula;

[0111] Step 603: Based on the inspection rule, compare the decision function g(k) with h. If g(k)≤h, no gas intrusion occurs. Otherwise, a gas intrusion condition occurs and an early warning is issued.

[0112] Therefore, the present invention adopts the above-mentioned gas intrusion monitoring method jointly driven by data and mechanism model, which has the following beneficial effects:

[0113] The present invention takes the pressure loss correction factor and the annulus outlet flow correction factor as sensitive characteristic parameters, and based on the unscented Kalman filter parameter estimation theory, establishes a new method for early monitoring of gas intrusion in controlled pressure drilling. The method amplifies the residual signal when complex situations occur downhole, which is more conducive to early monitoring of complex working conditions; and adopts the generalized likelihood ratio test algorithm to monitor online whether the residual signal changes, thereby reducing the dependence on technical personnel and being able to meet the early warning monitoring needs of gas intrusion and overflow in deep wells, ultra-deep wells and complex formations.

[0114] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0115] Figure 1 The present invention is a flowchart of the method.

[0116] Figure 2 This is a schematic diagram of the measurable pressure and flow data of managed pressure drilling.

[0117] Figure 3 Comparison between simulated measured values ​​and calculated values ​​of the gas intrusion monitoring model.

[0118] Figure 4 It is the estimated value of the annulus pressure loss correction factor and the annulus outlet flow correction factor during gas invasion.

[0119] Figure 5 This is the monitoring result of the gas intrusion monitoring method.

[0120] Figure 6 Comparison of gas intrusion monitoring results at different gas intrusion rates. DETAILED DESCRIPTION

[0121] In the description of the present invention, it is also necessary to explain that, unless otherwise clearly specified and limited, these embodiments are only used to illustrate the present invention and are not used to limit the scope of the present invention. In addition, it should be understood that after reading the content taught by the present invention, those skilled in the art make various changes or modifications to the present invention, and these equivalent forms also fall within the scope limited by the claims attached to the application.

[0122] like Figure 1 As shown, the present invention includes the following steps based on the joint driving of data and mechanism model:

[0123] Step 1, obtaining the basic parameters required for the simulated well;

[0124] Step 2, based on the drilling construction process, establish a single-phase flow hydraulic model of the wellbore under normal working conditions;

[0125] Step 3, introducing the outlet flow correction factor and the annular pressure loss correction factor into the single-phase flow hydraulic model under normal working conditions to construct a gas intrusion working condition monitoring model;

[0126] Step 4, collecting pressure data and flow data, and estimating the outlet flow correction factor and the annular pressure loss correction factor by using an unscented Kalman filter algorithm;

[0127] Step 5, calculating the decision function of the outlet flow correction factor and the annular pressure loss correction factor by using a generalized likelihood ratio test algorithm;

[0128] Step 6: By comparing the decision function value with the preset threshold, it is determined whether gas intrusion is likely to occur. If gas intrusion is likely to occur, a gas intrusion warning signal is issued.

[0129] Among them, the basic parameters described in step 1 include: well depth, sounding, well inclination, azimuth, well diameter, drill pipe inner diameter, drill pipe outer diameter, drill bit nozzle area, circulation displacement, drilling fluid density, formation density, drilling fluid plastic viscosity, drilling fluid specific heat, formation specific heat, inlet temperature, outlet temperature, geothermal gradient, formation thermal conductivity, drilling fluid thermal conductivity, and drill pipe thermal conductivity.

[0130] Furthermore, the specific steps of constructing the single-phase flow hydraulic model under normal operating conditions in step 2 are:

[0131] Step 201, setting the assumptions of the model;

[0132] Specifically, the assumptions of the single-phase flow hydraulic model under normal operating conditions include:

[0133] ①Only consider the one-dimensional flow along the flow direction in the drill string and annulus;

[0134] ② The thermophysical parameters of drilling fluid are constant, while the density ρ and viscosity μ of drilling fluid depend on pressure and temperature, expressed as ρ=ρ(p,T) and μ=μ(p,T), where p represents pressure and T represents temperature;

[0135] ③ The physical parameters of the formation (i.e. density, specific heat and thermal conductivity) are constant;

[0136] ④The concentration of cuttings in the drilling fluid is negligible and the average wellbore diameter is taken.

[0137] Step 202, establishing the control equation of the single-phase flow hydraulic model under normal operating conditions;

[0138] The control equations include the drilling fluid continuity equation in the drill string, the drilling fluid continuity equation in the annulus, the drilling fluid momentum conservation equation in the drill string, the drilling fluid momentum conservation equation in the annulus, the drilling fluid energy conservation equation in the drill string, the drill string energy conservation equation, the annulus energy conservation equation, the wellbore energy conservation equation, the formation energy conservation equation and the annulus outlet volume flow equation. The expressions of the above equations are as follows:

[0139] 1) Drilling fluid continuity equation in drill string:

[0140]

[0141] Among them, ρ l represents the fluid density, A represents the flow channel area, v l represents the fluid velocity; represents the deviator in the axial direction of the wellbore, represents the partial derivative of time; the subscript in represents the fluid in the column;

[0142] 2) Continuity equation of drilling fluid in annulus:

[0143]

[0144] Wherein, subscript a represents the annulus fluid;

[0145] 3) Drilling fluid momentum conservation equation in the drill string:

[0146]

[0147] Among them, p f represents pressure loss, p represents pressure, θ represents well inclination, and g represents gravitational acceleration constant;

[0148] 4) Drilling fluid momentum conservation equation in the annulus:

[0149]

[0150] 5) Drilling fluid energy conservation equation in the drill string:

[0151]

[0152] Among them, Q in represents the viscous heat source term of the drilling fluid in the drill string, q l Indicates the volume flow rate of the fluid; C l represents the specific heat capacity of the drilling fluid in the drill string, T in Indicates the temperature of the drilling fluid in the drill string, T w represents the temperature of the drill string, r pi Indicates the inner diameter of the drill string, h pi Indicates the heat transfer coefficient of the inner wall of the drill string;

[0153] 6) Drill pipe wall energy conservation equation:

[0154]

[0155] Among them, λ w represents the thermal conductivity of the drill string, ρ w Indicates the density of the drill string, C w is the specific heat of the drill string, T a represents the temperature of the annular fluid, r po Indicates the outer diameter of the drill string, h po Indicates the heat transfer coefficient of the outer wall of the drill string;

[0156] 7) Annular space energy conservation equation:

[0157]

[0158] Among them, Q a represents the viscous heat source term of the annular fluid, T c represents the temperature of the well wall, r ci represents the annulus radius, h ci It represents the heat transfer coefficient between the annulus and the wellbore wall;

[0159] 8) Wellbore energy conservation equation:

[0160]

[0161] Among them, λ c represents the thermal conductivity of the wellbore wall, ρ c Indicates the density of the wellbore wall, C c represents the specific heat of the wellbore wall, r co Represents the outer diameter of the wellbore unit;

[0162] 9) Energy conservation equation in the formation:

[0163]

[0164] Among them, T f represents the temperature of the formation, r represents the radial radius of the formation area from the wellbore axis, ρ f Indicates the density of the formation, C f represents the specific heat of the formation, λ f Indicates the thermal conductivity of the formation;

[0165] 10) The annulus outlet volume flow equation is:

[0166] q c =(Av l ) c (10)

[0167] Among them, q c is the annulus outlet volume flow rate, and the subscript c indicates the annulus outlet position;

[0168] Step 203, establishing an auxiliary equation of a single-phase flow hydraulic model under normal operating conditions;

[0169] The auxiliary equations include:

[0170] 1) Forced convection heat transfer coefficient calculation equation

[0171]

[0172] Nu=0.023Re 0.8 P r 13 (12)

[0173]

[0174] Where Nu is the Nusselt number, P r represents the Prandtl number, Re represents the Reynolds number, λ l Indicates the thermal conductivity of drilling fluid, μ l Indicates the viscosity of the drilling fluid, D pi / po / ci Respectively represent the inner diameter of the drill string, the outer diameter of the drill string and the annulus diameter, D hy Indicates the hydraulic diameter, D in the drill string hy =D pi , in the annulus D hy =D ci -D po ;

[0175] 2) Calculation equation of heat source term generated by fluid flow friction

[0176]

[0177] Where F is the friction coefficient, D hv represents the hydraulic diameter, p f Indicates the frictional pressure loss per unit length;

[0178] The friction coefficient F under laminar flow is:

[0179] F=16 / R e (16)

[0180] The friction coefficient F under laminar turbulent flow is:

[0181]

[0182] Among them, n g Represents the generalized flow index of the fluid;

[0183] Step 204, establish the flow characteristic equation of the flow through the wellhead throttle valve of the managed pressure drilling:

[0184]

[0185] Among them, q c represents the volume flow through the throttle valve, u c Indicates the throttle valve opening, k(u c ) represents the flow coefficient characteristic function of the throttle valve, ρ0 represents the density of clean water at 16°C, △p0 represents the pressure drop across the throttle valve when the opening is equal to 100%, and p c Indicates the pressure upstream of the throttle valve;

[0186] Furthermore, the steps of constructing the gas intrusion condition monitoring model in step 3 include:

[0187] Step 301, by analyzing the response characteristics of the initial gas invasion, three response characteristic parameters are defined, namely, annular space pressure change rate Annular pressure loss change rate and annulus outlet flow rate change rate The expressions are:

[0188]

[0189] in, Indicates the measured data of annular space pressure under gas invasion conditions. Indicates the measured data of annular space pressure under normal working conditions. Indicates the calculated data of annular pressure loss under normal working conditions. It represents the measured data of the annulus outlet flow rate under gas invasion conditions. Indicates the measurement data of the annulus outlet flow rate under normal working conditions;

[0190] Step 302, define the annulus pressure loss correction factor and the annulus outlet flow correction factor:

[0191]

[0192] Among them, θ a represents the annular pressure loss correction factor, θ q represents the annulus outlet flow correction factor;

[0193] The reference value of the annular pressure loss correction factor under normal conditions is 1, and its actual value deviates from the reference value when gas invasion occurs; the reference value of the annular outlet flow correction factor under normal conditions is also 1, and its actual value deviates from the reference value when gas invasion occurs.

[0194] Step 303: Set the annular pressure loss correction factor θ a and annulus outlet flow correction factor θ q They are introduced into the control equation (4) and control equation (10) of the single-phase flow hydraulic model under normal wellbore conditions, respectively, to quantitatively describe the magnitude of the annular pressure loss correction factor and the annular outlet flow correction factor when gas invasion occurs.

[0195] A gas invasion monitoring model based on the single-phase flow hydraulics model of normal wellbore conditions is established:

[0196] q c =θ q (Av l ) c

[0197]

[0198] Furthermore, the corresponding data is first measured by the sensor, and then the obtained measurement data is sent to the gas intrusion condition monitoring model. In the gas intrusion condition monitoring model, the unscented Kalman filter algorithm is used to infer the outlet flow correction factor θ from the measurement data. q and annulus pressure loss correction factor θ a , that is, the specific steps of step 4 are as follows:

[0199] Step 401, obtaining pressure data and flow data measurable by managed pressure drilling through a logging system arranged at the wellhead and a downhole PWD measuring tool;

[0200] Specifically, the pressure data includes the riser pressure, the bottom hole pressure in the drill string, the annulus bottom hole pressure, and the pressure upstream of the throttle valve; the flow data includes the riser pump displacement, the throttle valve downstream displacement, and the back pressure pump displacement;

[0201] Step 402, constructing a state equation and a measurement equation for managed pressure drilling, wherein the state equation represents the relationship between the state vectors at adjacent time points, and the measurement equation represents the relationship between the state vector and the measurement vector at the same time point;

[0202] The parameter to be estimated in the present invention is the annular pressure loss correction factor θ a and annulus outlet flow correction factor θ q , and since gas invasion is a gradual process,θ a and θ q The change is relatively slow, so the established state equation is shown as follows:

[0203] θ(k)=θ(k-1)+w(k) (24)

[0204] Where θ(k)=[θ a (k),θ q (k)], w(k)=[w1(k),w2(k)]∈R 2 represents process noise, k represents discrete time, a represents annular fluid, and q represents fluid flow rate;

[0205] The observed variables y(k) are the annulus bottomhole pressure, string bottomhole pressure, standpipe pressure, and annulus outlet flow rate, as shown in the following formula:

[0206] y(k)=[p a (k),p d (k),p s (k),q c (k)] (25)

[0207] Among them, p a Indicates the annulus bottom hole pressure, p d represents the bottom hole pressure of the drill string, p srepresents the riser pressure, q c Indicates the volume flow rate through the throttle valve;

[0208] The relationship between the observed variable y(k) and the state variable θ(k) is described by the gas intrusion condition monitoring model, and the measurement equation is established:

[0209] y(k)=F d (θ(k-1),u(k))+v k (26)

[0210] Among them, F d (·) is the finite difference solution form of the gas intrusion monitoring model, v k ∈R 4 is the measurement noise, u(k)=[q p (k),p c (k),q b (k)] represents the input vector, where q p Indicates the standpipe pumping capacity, p c Indicates the pressure upstream of the throttle valve, q b Indicates the displacement of the back pressure pump.

[0211] Further, the specific steps of constructing the decision function of the annular space outlet flow correction factor and the annular space pressure loss correction factor in step 5 are:

[0212] Step 501, based on the generalized likelihood ratio test algorithm, define two mutually exclusive propositions:

[0213] 1) Null hypothesis H0 (normal operating condition hypothesis)

[0214] H0:z~p(μ0;H0) (27)

[0215] 2) Alternative hypothesis H1 (gas intrusion condition hypothesis)

[0216] H1:z~p(μ1;H1) (28)

[0217] Among them, p(μ i ;H i ) is the probability density function of variable z under the specified hypothesis, μ i is the sequence mean parameter of variable z under the specified assumptions.

[0218] Step 502, assuming that the change occurs at time j, the sum of the log-likelihood ratios is as follows:

[0219]

[0220] in, represents the sum of log-likelihood ratios;

[0221] Step 503, since j and μ1 are unknown in advance, the maximum likelihood estimates of j and μ1 are substituted into equation (29), and the generalized likelihood test decision function is as follows:

[0222]

[0223] Step 504, for a Gaussian distribution random sequence with a data window length of N, the established decision function is shown in the following formula:

[0224]

[0225] Further, the gas intrusion warning step in step 6 includes:

[0226] Step 601, the judgment threshold is recorded as h, and the inspection rule is defined as shown in the following formula:

[0227] g(k)≤h, accept H0

[0228] g(k)>h, reject H0 (33)

[0229] Since the threshold h is closely related to the significance level α, the relationship between the two is expressed by the following formula:

[0230] p{g(k)>h|H0}≤α (34)

[0231] Step 602, statistically analyze the probability distribution of the Gaussian distribution random sequence before the known change occurs, pre-set the significance level α, and determine the threshold h using formula (34);

[0232] Step 603, by comparing the decision function g(k) with h, determine whether to issue a gas intrusion warning. When g(k)≤h, no gas intrusion occurs. Otherwise, a gas intrusion condition occurs and a warning is issued.

[0233] Example

[0234] The present invention proposes a gas invasion monitoring method jointly driven by data and mechanism models. Based on the sensitivity analysis results of characteristic parameters of early response to gas invasion conditions, an early gas invasion monitoring method based on a wellbore hydraulic model under normal conditions is established. By introducing an annular pressure loss correction factor and annular outlet flow correction factor into the wellbore hydraulic model under normal conditions, the annular pressure loss change and the annular outlet flow change under gas invasion conditions are quantitatively characterized, thereby forming a gas invasion condition monitoring model, and the above correction factors are estimated in real time based on the measured data using the unscented Kalman filter algorithm (UKF), and then the generalized likelihood ratio test algorithm (GLRT) is used to determine whether the correction factors have changed, thereby identifying whether gas invasion has occurred underground.

[0235] Figure 2The measurable pressure and flow data of the managed pressure drilling obtained in this embodiment are expressed as follows in the MPD operation by combining the downhole pressure measuring tool and the surface logging equipment:

[0236] p(t)=[p s (t),p d (t),p a (t),p c (t)] (35)

[0237] q(t)=[q s (t),q c (t),q b (t)] (36)

[0238] In the formula, p s Indicates the standpipe pressure, p d represents the bottom hole pressure of the drill string, p a Indicates the annulus bottom hole pressure, p c Indicates the pressure upstream of the throttle valve, q s represents the flow rate of the riser pump, q c represents the flow rate downstream of the throttle valve, q b Indicates the back pressure pump displacement.

[0239] This embodiment takes into account the performance of the drilling fluid physical properties as the temperature and pressure change, and performs numerical simulation verification. The specific parameter settings are shown in Table 1.

[0240] Table 1 Numerical simulation parameter settings

[0241]

[0242] Figure 3 The comparison between the simulated measured values ​​of the annulus bottomhole pressure, the string bottomhole pressure, the riser pressure and the annulus outlet flow rate in the embodiment of the present invention and the calculated values ​​of the gas intrusion monitoring model; wherein the calculated values ​​of the gas intrusion monitoring model refer to the annulus pressure loss correction factor and the annulus outlet flow rate correction factor estimated based on the UKF algorithm, and the model calculated values ​​obtained by using the gas intrusion condition monitoring model. Figure 3It can be seen from the figure that the calculated value of the gas invasion monitoring model can quickly and accurately track the simulated measurement value. In the normal circulation stage (t = 0-750s), the simulated measurement data of pressure and flow rate have small fluctuations. This fluctuation is caused by the consideration of the changes in the physical properties of the drilling fluid with temperature and pressure, and is also the result of random noise. In the early stage of gas invasion, as shown in the period of t = 750s-1500s, the gas invasion into the wellbore leads to a decrease in the static pressure of the gas-bearing well section. At the same time, because the wellbore is connected to the high-pressure reservoir, the drilling fluid flow rate in the wellbore is accelerated, resulting in an increase in the pressure loss along the annulus. The simulated pressure measurement data will rise slightly at the initial moment of gas invasion. At the same time, due to the compressibility of gas in the high-pressure environment at the bottom of the well, the change in the simulated outlet flow rate data is not obvious, and it is difficult to monitor gas invasion through the outlet flow rate. With the further development of gas invasion, as shown in the curve after t = 1500s, the changes in the simulated measurement data of pressure and flow rate are beginning to emerge, and the trend is becoming more and more obvious. The simulated pressure measurement value gradually decreases with time, while the simulated flow measurement value gradually increases.

[0243] Figure 4 are the estimated values ​​of the annulus pressure loss correction factor and the annulus outlet flow correction factor during gas invasion in this embodiment. In the normal circulation stage, the pressure loss correction factor and the flow correction factor fluctuate around the reference value. Between t=750s-1000s, the annulus pressure loss correction factor shows a rapid increase trend, which is consistent with the phenomenon of increased annulus pressure in the early stage of gas invasion; afterwards, the annulus pressure loss correction factor continues to decrease, which is the result of the continuous decrease in the annulus bottomhole pressure due to the gas entering the wellbore. During the entire gas invasion stage, the annulus outlet flow correction factor continues to increase, with a slower growth rate between t=750-1500s, and after t=2000s, the growth rate becomes significantly faster, increasing approximately exponentially.

[0244] Figure 5 is the monitoring result of the gas intrusion monitoring method in this embodiment. It can be seen from the figure that in the normal cycle stage, the value of the generalized likelihood test decision function is always less than the gas intrusion condition discrimination threshold; with the appearance of gas intrusion at t = 750s, the calculated value of the decision function gradually increases and quickly exceeds the accident discrimination threshold, such as Figure 5 The method detected the occurrence of gas intrusion at t = 825s, while the set gas intrusion occurrence time was t = 750s, which shows that the new gas intrusion monitoring method has the ability to quickly identify gas intrusion conditions.

[0245] Figure 6The following is a comparison of the gas intrusion monitoring results at different gas intrusion rates in this embodiment. The flow difference method and the mud pool increment method are commonly used methods for monitoring gas intrusion. The numerical simulation results show that the time when the flow difference method detects gas intrusion is t=1920s, while the time when the mud pool increment method detects gas intrusion is more delayed, and the alarm threshold is not reached until t=2040s. The alarm threshold of the flow difference method set during the simulation is 0.55L / s, and the alarm threshold of the mud pool increment method is 0.5m 3 .from Figure 6 It can be seen that under different gas invasion rates (0.04-0.15m3 / s), the time required for confirming gas invasion by this method is significantly less than that by the flow difference method and the mud pool increment method, and the advantage of this method is more obvious at small gas invasion rates; with the increase of gas invasion rate, the time required for confirming gas invasion by this method almost no longer decreases; at small gas invasion rates, the advantage of the flow difference method over the mud pool increment method is not obvious, and with the increase of gas invasion rate, the advantage of the flow difference method becomes more and more obvious.

[0246] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that they can still modify or replace the technical solution of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical solution to deviate from the spirit and scope of the technical solution of the present invention.

Claims

1. A gas intrusion monitoring method driven jointly by data and mechanism model, characterized in that: The following steps are involved: Step 1: Obtain the basic parameters required for the simulated well; Step 2: Based on the drilling construction technology, a single-phase flow hydraulic model of the wellbore under normal working conditions is constructed; Step 3: Based on the single-phase flow hydraulics model of normal wellbore conditions, the outlet flow correction factor and the annular pressure loss correction factor are introduced to construct a gas invasion condition monitoring model; Step 4: Collect pressure data and flow data, and estimate the outlet flow correction factor and annular pressure loss correction factor by using the unscented Kalman filter algorithm; Step 5: construct the decision functions of the annular outlet flow correction factor and the annular pressure loss correction factor respectively through the generalized likelihood ratio test algorithm; Step 6: By comparing the decision function value with the preset threshold, determine whether gas intrusion is likely to occur. If so, issue a gas intrusion warning.

2. The gas intrusion monitoring method driven by data and mechanism model as claimed in claim 1, characterized in that: The basic parameters described in step 1 include: well depth, sounding, well inclination, azimuth, well diameter, drill pipe inner diameter, drill pipe outer diameter, drill bit nozzle area, circulation displacement, drilling fluid density, formation density, drilling fluid plastic viscosity, drilling fluid specific heat, formation specific heat, inlet temperature, outlet temperature, geothermal gradient, formation thermal conductivity, drilling fluid thermal conductivity, and drill pipe thermal conductivity.

3. The gas intrusion monitoring method driven by data and mechanism model as claimed in claim 1, characterized in that: The specific steps of step 2 include: Step 201: defining the assumptions of the single-phase flow hydraulic model under normal wellbore conditions; Step 202: Based on the assumptions, establish the control equations of the single-phase flow hydraulic model under normal wellbore conditions; Step 203: establishing auxiliary equations of a single-phase flow hydraulic model under normal wellbore conditions; Step 204: Establish a flow characteristic equation for the flow through the wellhead throttle valve of the managed pressure drilling: Among them, q c represents the volume flow through the throttle valve, u c Indicates the throttle valve opening, k(u c ) represents the flow coefficient characteristic function of the throttle valve, ρ0 represents the density of clean water at 16°C, △p0 represents the pressure drop across the throttle valve when the opening is equal to 100%, and p c Indicates the pressure upstream of the throttle valve.

4. A gas intrusion monitoring method driven jointly by data and mechanism model as claimed in claim 3, characterized in that: The assumptions defined in step 201 include: 1) Only one-dimensional flow along the flow direction in the drill string and annulus is considered; 2) The thermophysical parameters of the drilling fluid are constant. The density ρ and viscosity μ of the drilling fluid depend on the pressure and temperature, which can be expressed as ρ = ρ(p, T) and μ = μ(p, T), where p represents pressure and T represents temperature; 3) The density, specific heat and thermal conductivity of the formation are constant; 4) The concentration of cuttings in the drilling fluid is negligible and the average wellbore diameter is taken.

5. The gas intrusion monitoring method driven by data and mechanism model as claimed in claim 4, characterized in that: The control equations established in step 202 include: 1) Drilling fluid continuity equation in drill string Among them, ρ l represents the fluid density, A represents the flow channel area, v l represents the fluid velocity; represents the deviator in the axial direction of the wellbore, represents the partial derivative of time; the subscript in represents the fluid in the column; 2) Continuity equation of drilling fluid in annulus Where a represents the annulus fluid; 3) Drilling fluid momentum conservation equation in the drill string Among them, p f represents pressure loss, θ represents well inclination, and g represents gravitational acceleration constant; 4) Drilling fluid momentum conservation equation in the annulus 5) Drilling fluid energy conservation equation in drill string Among them, Q p represents the viscous heat source term of the drilling fluid in the drill string, q l Represents the volume flow rate of the fluid, C l represents the specific heat capacity of the drilling fluid in the drill string, T p Indicates the temperature of the drilling fluid in the drill string, T w represents the temperature of the drill string, r pi Indicates the inner diameter of the drill string, h pi Indicates the heat transfer coefficient of the inner wall of the drill string; 6) Drill pipe wall energy conservation equation Among them, λ w represents the thermal conductivity of the drill string, ρ w Indicates the density of the drill string, C w is the specific heat of the drill string, T a represents the temperature of the annular fluid, r po Indicates the outer diameter of the drill string, h po Indicates the heat transfer coefficient of the outer wall of the drill string; 7) Annular space energy conservation equation Among them, Q a represents the viscous heat source term of the annular fluid, T c represents the temperature of the well wall, r ci represents the annulus radius, h ci It represents the heat transfer coefficient between the annulus and the wellbore wall; 8) Wellbore energy conservation equation Among them, λ c represents the thermal conductivity of the wellbore wall, ρ c Indicates the density of the wellbore wall, C c represents the specific heat of the wellbore wall, r co Represents the outer diameter of the wellbore unit; 9) Energy conservation equation in formations Among them, T f represents the temperature of the formation, r represents the radial radius of the formation area from the wellbore axis, ρ f Indicates the density of the formation, C f represents the specific heat of the formation, λ f Indicates the thermal conductivity of the formation; 10) Annulus outlet volume flow equation q c =(Off l ) c Among them, q c is the annulus outlet volume flow rate, and the subscript c represents the annulus outlet position.

6. A gas intrusion monitoring method driven jointly by data and mechanism model as claimed in claim 5, characterized in that: The auxiliary equations in step 203 include: 1) Forced convection heat transfer coefficient calculation equation No=0.023Re 0.8 P r 13 Where Nu is the Nusselt number, P r represents the Prandtl number, Re represents the Reynolds number, λ l Indicates the thermal conductivity of drilling fluid, μ l Indicates the viscosity of the drilling fluid, D pi / po / ci Respectively represent the inner diameter of the drill string, the outer diameter of the drill string and the annulus diameter, D hy Indicates the hydraulic diameter, D in the drill string hy =D pi , in the annulus D hy =D ci -D po ; 2) Calculation equation of heat source term generated by fluid flow friction Where F is the friction coefficient, D hv represents the hydraulic diameter, p f Indicates the frictional pressure loss per unit length; The friction coefficient F under laminar flow is: F=16 / R e The friction coefficient F under laminar turbulent flow is: Among them, n g Represents the generalized flow index of the fluid.

7. A gas intrusion monitoring method driven jointly by data and mechanism model as claimed in claim 6, characterized in that: The specific steps of step 3 include: Step 301: Define the response characteristic parameters of the gas intrusion condition monitoring model: in, represents the annular pressure change rate, represents the annular pressure loss change rate, It represents the rate of change of annulus outlet flow rate; Indicates the measured data of annular space pressure under gas invasion conditions. Indicates the measured data of annular space pressure under normal working conditions. Indicates the calculated data of annular pressure loss under normal working conditions. It represents the measured data of the annulus outlet flow rate under gas invasion conditions. Indicates the measurement data of the annulus outlet flow rate under normal working conditions; Step 302: Based on the response characteristic parameters, define the annulus pressure loss correction factor and the annulus outlet flow correction factor: Among them, θ a represents the annular pressure loss correction factor, θ q represents the annulus outlet flow correction factor; Step 303: Set θ a and θ q They are introduced into the drilling fluid momentum conservation equation in the annulus and the annulus outlet volume flow equation respectively, thus establishing a gas invasion condition monitoring model: q c =θ q (Of l ) c 8. The gas intrusion monitoring method driven by data and mechanism model as claimed in claim 7, characterized in that: The specific steps of step 4 include: Step 401: Obtain pressure data and flow data measurable by managed pressure drilling through a logging system arranged at the wellhead and a downhole PWD measurement tool; Step 402: Construct the state equation and measurement equation for managed pressure drilling: 1) Equation of state θ(k)=θ(k-1)+w(k) Where θ(k)=[θ a (k),θ q (k)], w(k)=[w1(k),w2(k)]∈R 2 represents process noise, k represents discrete time, a represents annular fluid, and q represents fluid flow rate; 2) Measurement equation y(k)=F d (θ(k-1),u(k))+v k Among them, F d (·) represents the finite difference solution form of the gas intrusion monitoring model; v k ∈R 4 represents the measurement noise; u(k)=[q p (k),p c (k),q b (k)] represents the input vector, q p Indicates the standpipe pumping capacity, p c Indicates the pressure upstream of the throttle valve, q b represents the displacement of the back pressure pump; y(k) represents the observed variable, and the observed variable is the annulus bottom hole pressure p a , string bottom hole pressure p d , Standpipe pressure p s and the displacement downstream of the throttle valve q c ; Step 403: Based on the measurement data obtained in step 401, the unscented Kalman filter algorithm is used to infer θ based on the gas intrusion condition monitoring model. a and θ q .

9. A gas intrusion monitoring method driven jointly by data and mechanism model as claimed in claim 8, characterized in that: Step 5 The steps to construct the decision function include: Step 501: Based on the generalized likelihood ratio test algorithm, define two mutually exclusive propositions: 1) Normal operating condition assumption H0 H0:z~p(μ0;H0) 2) Gas intrusion condition assumption H1 H1:z~p(μ1;H1) Among them, p(μ i ;H i ) is the probability density function of variable z under the specified hypothesis, μ i is the sequence mean parameter of variable z under the specified assumption; Step 502: If the change occurs at time j, the sum of the log-likelihood ratios is: in, represents the sum of log-likelihood ratios; Step 503: Substitute the maximum likelihood estimates of j and μ1 into the above formula to obtain the generalized likelihood test decision function: Step 504: For a Gaussian distribution random sequence with a data window length of N, establish the following decision function:

10. A gas intrusion monitoring method driven jointly by data and mechanism model as claimed in claim 9, characterized in that: The specific steps of step 6 include: Step 601: Define the following inspection rules: g(k)≤h, accept H0 g(k)>h, reject H0 Among them, h represents the judgment threshold, g(k) represents the decision function; The relationship between the threshold h and the significance level α is expressed by the following formula: p{g(k)>h|H0}≤α; Step 602: Count the probability distribution of the Gaussian distribution random sequence before the known change occurs, pre-set the significance level α, and determine h through the above formula; Step 603: Based on the inspection rule, compare the decision function g(k) with h. If g(k)≤h, no gas intrusion occurs. Otherwise, a gas intrusion condition occurs and an early warning is issued.

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