REAL-TIME INVERSION METHOD OF MULTI-MEASUREMENT POINT AND MULTI-PARAMETER DRILLING, DEVICE, MEDIA AND PRODUCT

Description

CROSS-REFERENCE TO RELATED APPLICATION

This patent application claims the benefit and priority of Chinese Patent Application No. 202411067504.5 filed with the China National Intellectual Property Administration on Aug. 5, 2024, the disclosure of which is incorporated by reference herein in its entirety as part of the application.

TECHNICAL FIELD

The present disclosure relates to the technical field of oil-gas exploration, in particular to a real-time inversion method of multi-measurement point and multi-parameter drilling, a device, a media and a product.

BACKGROUND

With the long-term exploration and exploitation of middle-shallow oil gas field, it has become more and more difficult to make a big breakthrough in the middle-shallow layer of the basin, and the target layer of oil-gas exploration has gradually shifted from a middle-shallow layer to a deep layer and an ultra-deep layer. It is of great significance to realize the replacement of deep and ultra-deep oil and gas energy for energy security and sustainable development. The main characteristics of such resource reservoirs include: deep burial, complex formation lithology, stress sensitivity, high temperature and high pressure. Frequent kick and lost circulation incidents occur during exploration and development drilling, leading to high drilling costs and significant safety risks. The problem of gas kick resulted from an abnormal high pressure is particularly prominent, and the safety hazard is huge, which is objectively difficult to avoid. Once it is not warned and controlled in time, the downhole gas kick condition will further deteriorate, and eventually develop into a major accident of uncontrollable blowout, resulting in huge losses. Therefore, the parameters such as the wellbore pressure and the temperature are monitored in real time to achieve accurate quantitative interpretation of the downhole gas kick condition, which is conducive to reducing operation risks and avoiding major accidents and is of great significance to promoting the development of oil and gas resources.

In modern drilling operations, the Measurement While Drilling (MWD) technology and the Logging While Drilling (LWD) technology have become important means to accurately control borehole trajectories and efficiently develop oil and gas reservoirs. In order to monitor downhole working conditions accurately and prevent downhole spillover effectively, field operators usually use a Pressure While Drilling (PWD) tool to collect and analyze some engineering parameters in the well in real time. However, the existing measuring tools can only evaluate the borehole environment near the drill bit, and cannot evaluate the whole borehole comprehensively. Therefore, the ground operators cannot grasp the details inside the wellbore in real time, so that they cannot make the most reasonable response measures in time. In addition, the traditional data acquisition methods can only provide limited information in the well, and judges the downhole situation depending on the data of a single measurement point, which ignores the diversity and dynamics of various positions and depths in the well to a large extent. At the same time, due to a lot of fuzziness, randomness and uncertainty in the complex formation, the wrong control measures resulted from unclear understanding of the downhole gas kick condition will lead to more complicated situations and even serious accidents.

SUMMARY

The purpose of the present disclosure is to provide a real-time inversion method of multi-measurement point and multi-parameter drilling, a device, a media and a product, which can realize an accurate quantitative interpretation of a downhole gas kick condition of deep and ultra-deep drilling.

In order to achieve the above purpose, the present disclosure provides the following scheme.

In a first aspect, the present disclosure provides a real-time inversion method of multi-measurement point and multi-parameter drilling, including:

• • acquiring downhole measurement data of target drilled well; where the downhole measurement data is measured in real time based on an intelligent microsphere along a drill pipe;
  • establishing a real-time inversion interpretation model for a downhole gas kick condition based on a wellbore multiphase flow forward model and an adaptive unscented Kalman filter algorithm according to the downhole measurement data; where the wellbore multiphase flow forward model is a mathematical model which is constructed for a heat transfer and mass transfer process of wellbore multiphase flow resulted from a downhole gas kick condition based on a theory of wellbore multiphase flow and heat transfer; the adaptive unscented Kalman filter algorithm is an optimal estimator suitable for nonlinear time-varying noise interference which is constructed after introducing an adaptive factor into an unscented Kalman filter; and
  • inputting the downhole measurement data into the real-time inversion interpretation model for the downhole gas kick condition to obtain an inversion parameter of the target drilled well.

In an embodiment, the downhole measurement data includes wellhead pressure sensor data, downhole temperature sensor data, downhole pressure sensor data, inlet flow sensor data and outlet flow sensor data.

In an embodiment, formulas of the downhole measurement data is expressed as follows:

p m , N = [ p m , N ( t 1 ) , p m , N ( t 2 ) , … , p m , N ( t N ) ] ; T m , N = [ T m , N ( t 1 ) , T m , N ( t 2 ) , … , T m , N ( t N ) ] ; p m , c = [ p m , c ( t 1 ) , p m , c ( t 2 ) , … , p m , c ( t N ) ] ; q m , c = [ q m , c ( t 1 ) , q m , c ( t 2 ) , … , q m , c ( t N ) ] ; q m , s = [ q m , s ( t 1 ) , q m , s ( t 2 ) , … , q m , s ( t N ) ] ;

where p denotes a wellbore pressure, t₁, t₂, . . . , t_N denote N time points corresponding to the measurement data, T denotes temperature, q denotes a flow rate, subscript m denotes the measurement data, subscript N denotes a downhole measurement point N, subscript c denotes a wellhead, and subscript s denotes a surface outlet.

In an embodiment, the inversion parameter includes a gas kick position, a gas kick rate, a gas front edge height, a gas slippage velocity and an invaded gas volume.

In an embodiment, the acquiring downhole measurement data of target drilled well includes:

based on the intelligent microspheres along a wireless measurement sensor device provided at each node in the drill pipe, acquiring the downhole measurement data collected in each intelligent microsphere which is in data communication with the wireless measurement sensor device.

In an embodiment, subsequent to the acquiring downhole measurement data of target drilled well, the method further includes:

• • carrying out data preprocessing on the downhole measurement data, which includes:
  • using a statistical method to check whether there are missing values and/or abnormal values in the downhole measurement data;
  • if so, filling the missing values and/or abnormal values by an average filling method to obtain filled downhole measurement data;
  • carrying out data denoising and normalization processing on the filled downhole measurement data to obtain processed downhole measurement data.

In an embodiment, the real-time inversion interpretation model for the downhole gas kick condition is expressed as follows:

x k / k - 1 ( i ) = f ⁢ ( x k - 1 / k - 1 ( i ) ) + q k = f ⁢ ( [ h kick , q kick , L G , v G , V G ] k - 1 / k - 1 ( i ) ) +  [ ⁠ ( q h kick ) k 0 0 0 0 0 ( q q kick ) k 0 0 0 0 0 ( q L G ) k 0 0 0 0 0 ( q v G ) k 0 0 0 0 0 ( q V G ) k ] ; y k / k - 1 ( i ) = g ⁢ ( x k / k - 1 ( i ) ) + r k = [ p m , N ( [ h kick , q kick , L G , v G , V G ] k / k - 1 ( i ) ) T m , N ( [ h kick , q kick , L G , v G , V G ] k / k - 1 ( i ) ) p m , c ⁢ ( [ h kick , q kick , L G , v G , V G ] k / k - 1 ( i ) ) q m , c ( [ h kick , q kick , L G , v G , V G ] k / k - 1 ( i ) ) q m , s ( [ h kick , q kick , L G , v G , V G ] k / k - 1 ( i ) ) ] +  [ ⁠ ( v p m , N ) k 0 0 0 0 0 ( v T m , N ) k 0 0 0 0 0 ( v p m , c ) k 0 0 0 0 0 ( v q m , c ) k 0 0 0 0 0 ( v q m , s ) k ] ;

where p denotes the wellbore pressure, t₁, t₂, . . . , t_N denote the N time points corresponding to the measurement data, T denotes the temperature, q denotes the flow rate, the subscript m denotes the measurement data, the subscript N denotes the downhole measurement point N, the subscript c denotes the wellhead, the subscript s denotes the surface outlet, h denotes the gas kick position, q denotes the gas kick rate, L denotes the gas front edge height, v denotes the gas slippage velocity, V denotes the invaded gas volume, subscripts kick and G denote the downhole gas kick condition, and subscript k denotes a k-th.

In a second aspect, the present disclosure provides a computer device, including: a memory, a processor and a computer program which is stored in the memory and is executable on the processor, where the processor executes the computer program to implement the real-time inversion method of multi-measurement point and multi-parameter drilling described in any one of the above.

In a third aspect, the present disclosure provides a nonvolatile computer-readable storage medium on which a computer program is stored, where the computer program, when executed by a processor, implements the real-time inversion method of multi-measurement point and multi-parameter drilling described in any one of the above.

In a fourth aspect, the present disclosure provides a computer program product, including a computer program, where the computer program, when executed by a processor, implements the real-time inversion method of multi-measurement point and multi-parameter drilling described in any one of the above.

According to the specific embodiment provided by the present disclosure, the present disclosure discloses the following technical effects.

The present disclosure provides a real-time inversion method of multi-measurement point and multi-parameter drilling, a device, a media and a product. The method includes: acquiring downhole measurement data of target drilled well; where the downhole measurement data is measured in real time based on an intelligent microsphere along a drill pipe; establishing a real-time inversion interpretation model for a downhole gas kick condition based on a wellbore multiphase flow forward model and an adaptive unscented Kalman filter algorithm according to the downhole measurement data; where the wellbore multiphase flow forward model is a mathematical model which is constructed for a heat transfer and mass transfer process of wellbore multiphase flow resulted from a downhole gas kick condition based on a theory of wellbore multiphase flow and heat transfer; the adaptive unscented Kalman filter algorithm is an optimal estimator suitable for nonlinear time-varying noise interference which is constructed after introducing an adaptive factor into the unscented Kalman filter; and inputting the downhole measurement data into the real-time inversion interpretation model for the downhole gas kick condition to obtain an inversion parameter of the target drilled well. The present disclosure establishes a real-time inversion interpretation model for a downhole gas kick condition with reference to the constructed wellbore multiphase flow forward model and the adaptive unscented Kalman filter algorithm based on the downhole measurement data, thereby realizing the accurate quantitative interpretation of a downhole gas kick condition of deep and ultra-deep drilling, and thus effectively preventing and reducing the occurrence probability of downhole complex conditions and improving the comprehensive efficiency of drilling operations.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to explain the embodiments of the present disclosure or the technical schemes in the prior art more clearly, the drawings that need to be used in the embodiments will be briefly introduced hereinafter. Obviously, the drawings in the following description are only some embodiments of the present disclosure. For those skilled in the art, other drawings can be obtained according to these drawings without creative labor.

FIG. 1 is a schematic diagram showing an application environment of a real-time inversion method of multi-measurement point and multi-parameter drilling according to an embodiment of the present disclosure.

FIG. 2 is a flowchart of a real-time inversion method of multi-measurement point and multi-parameter drilling according to an embodiment of the present disclosure.

FIG. 3 is a schematic diagram of a real-time measurement and transmission device along a drill pipe based on an intelligent microsphere according to an embodiment of the present disclosure.

FIG. 4 is a schematic diagram of an operation control system according to an embodiment of the present disclosure.

FIG. 5 is a flowchart of an adaptive unscented Kalman filter algorithm according to an embodiment of the present disclosure.

FIG. 6 is a flowchart of inversion interpretation modeling of a downhole gas kick condition according to an embodiment of the present disclosure.

FIG. 7 is a schematic structural diagram of a computer device according to an embodiment of the present disclosure.

DETAILED DESCRIPTION OF THE EMBODIMENTS

The technical schemes in the embodiments of the present disclosure will be clearly and completely described with reference to the drawings in the embodiments of the present disclosure hereinafter. Obviously, the described embodiments are only some embodiments of the present disclosure, rather than all of the embodiments. Based on the embodiment of the present disclosure, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present disclosure.

In order to make the above objects, features and advantages of the present disclosure more obvious and understandable, the present disclosure will be explained in further detail with reference to the drawings and detailed description hereinafter.

The real-time inversion method of multi-measurement point and multi-parameter drilling according to the embodiment of the present disclosure can be applied to the application environment as shown in FIG. 1. The terminal 102 is communicated with the server 104 through a network to transmit the downhole measurement data for further processing. The data storage system may be provided separately, integrated on the server 104, or placed on the cloud or other servers. The terminal 102 may send the downhole measurement data to the server 104. After receiving the downhole measurement data, the server 104 processes the downhole measurement data and acquires downhole measurement data of target drilled well; where the downhole measurement data is measured in real time based on the intelligent microsphere along the drill pipe.

A real-time inversion interpretation model for a downhole gas kick condition is established based on a wellbore multiphase flow forward model and an adaptive unscented Kalman filter algorithm according to the downhole measurement data; where the wellbore multiphase flow forward model is a mathematical model which is constructed for a heat transfer and mass transfer process of wellbore multiphase flow resulted from a downhole gas kick condition based on a theory of wellbore multiphase flow and heat transfer; the adaptive unscented Kalman filter algorithm is an optimal estimator suitable for nonlinear time-varying noise interference which is constructed after introducing an adaptive factor into the unscented Kalman filter.

The downhole measurement data is input into the real-time inversion interpretation model for the downhole gas kick condition to obtain an inversion parameter of the target drilled well. The server 104 can feed back the obtained inversion parameters of the target drilled well to the terminal 102. In addition, in some embodiments, the real-time inversion method of multi-measurement point and multi-parameter drilling can also be implemented by the server 104 or the terminal 102 alone. For example, the terminal 102 can directly process the downhole measurement data to be processed, or the server 104 can acquire the downhole measurement data to be processed from a data storage system and process the downhole measurement data to be processed.

The terminal 102 may be, but not limited to, various desktop computers, notebook computers, smart phones, tablet computers, Internet of Things devices and portable wearable devices. The Internet of Things devices may be smart speakers, smart TVs, smart air conditioners, smart vehicle-mounted devices and the like. The portable wearable devices may be smart watches, smart bracelets, headsets, etc. The server 104 may be implemented by an independent server or a server cluster consisted of a plurality of servers, and may also be a cloud server.

In an exemplary embodiment, as shown in FIG. 2, a real-time inversion method of multi-measurement point and multi-parameter drilling is provided. The method can be executed by a computer device, specifically by the computer device such as a terminal or a server alone, or by both the terminal and the server. In the embodiment of the present disclosure, taking the application of the method to the server 104 in FIG. 1 as an example, the method includes steps 201 to 203.

Step 201, the downhole measurement data of target drilled well is acquired; where the downhole measurement data is measured in real time based on the intelligent microsphere along a drill pipe.

Step 202, a real-time inversion interpretation model for a downhole gas kick condition is established based on a wellbore multiphase flow forward model and an adaptive unscented Kalman filter algorithm according to the downhole measurement data; where the wellbore multiphase flow forward model is a mathematical model which is constructed for a heat transfer and mass transfer process of wellbore multiphase flow resulted from a downhole gas kick condition based on a theory of wellbore multiphase flow and heat transfer; the adaptive unscented Kalman filter algorithm is an optimal estimator suitable for nonlinear time-varying noise interference which is constructed after introducing an adaptive factor into the unscented Kalman filter.

Step 203, the downhole measurement data is input into the real-time inversion interpretation model for the downhole gas kick condition to obtain the inversion parameter of the target drilled well.

Step 201 specifically includes:

based on the intelligent microspheres, along wireless measurement sensor devices provided at each node in the drill pipe, acquiring downhole measurement data collected in each intelligent microsphere which is in data communication with the wireless measurement sensor device.

The downhole measurement data includes wellhead pressure sensor data, downhole temperature sensor data, downhole pressure sensor data, inlet flow sensor data and outlet flow sensor data.

Specifically, a real-time measurement and transmission device along a drill pipe based on intelligent microspheres uses a conventional drill string combined with an improved measurement and transmission tool, and arranges a plurality of wireless sensors at the same interval on the drill string along the longitudinal direction. Various state parameters at different positions of the wellbore in key well sections can be monitored at the same time, a distributed sensor network is formed, and multi-point real-time measurement of engineering parameters such as temperature and pressure can be realized, so as to establish a complete view of parameters of a certain drilling along the drill string, and make up for the defect in the prior art that the parameters are only measured in a single place at a single time.

FIG. 3 shows a real-time measurement and transmission device along a drill pipe based on intelligent microspheres in this embodiment. As shown in FIG. 3, the drilling device includes a receiving device installed on the ground, a conventional drilling platform, a drill pipe extending into the wellbore, and a real-time measurement and transmission device along the drill pipe based on intelligent microspheres. The real-time measurement and transmission device along the drill pipe based on intelligent microspheres includes a multi-parameter wireless measurement sensor installed on the drill pipe, the intelligent microsphere and a mechanical arm installed at the wellhead.

The specific implementation method of the real-time measurement and transmission device along a drill pipe based on intelligent microspheres is as follows.

• • 1) The conventional drill pipe with N nodes is divided into N−1 control volumes, and each of the control volumes follows the laws of conservation of mass, momentum and energy. Every unexpected change of parameters such as the flow rate in the control volume will lead to the change of the node pressure.
  • 2) A wireless measurement sensor device is embedded in the conventional drill pipe. The selected sensor is a multi-parameter wireless measurement sensor. The sensors are arranged according to a certain control volume, as shown in FIG. 3. The measurement sensors are set on the drill pipe at a spacing of 50 m along the longitudinal direction. However, the measurement sensors may also be arranged in the longitudinal direction in a non-uniform manner. For example, the measurement sensors are arranged more densely in more important well sections, which may measure more downhole data to ensure that the downhole conditions can be truly and accurately reflected.
  • 3) During drilling, after the drill bit reaches a certain depth or reaches a specific formation, the operator will use the clamper on the mechanical arm to clamp the microspheres and distribute the microspheres to the specified measurement sensors when making connection. Specifically, in the process of launching intelligent microspheres, the drill pipe is usually partially drilled into the ground, but the intelligent microsphere should be launched when making connection (that is, one drill pipe is added at a time). When the drill pipe reaches an opportunity of making connection, an operator is ready to add a drill pipe with multi-parameter wireless measurement sensors. The mechanical arm clamps the intelligent microspheres and precisely aligns them with the sensor slots on the drill pipe. The operator monitors the operation, including parameters such as the position, the angle and the velocity, of the mechanical arm in real time through the operation control system, and controls the mechanical arm to put intelligent microspheres into each sensor position on the drill pipe. Through wireless communication technology, it is confirmed whether the connection between the intelligent microsphere and the drill pipe sensor is successful, and it is ensure that the data transmission is normal. After launching, the drill pipe will continue to drill, and the next drilling operation will be carried out.
  • 4) The wireless connection between the intelligent microspheres and the measurement sensor is implemented through near-field induction. The sensor stores the measurement data in the microspheres. Through mutual communication and synchronous measurement among the microspheres, a distributed array of microspheres is formed. Using different time segments in the same frequency band, a plurality of intelligent microspheres wirelessly transmit data to a ground receiving device through a micro wireless communication module and a related network protocol.
  • 5) The ground information processing device can construct a state of key well sections according to the received information, and form an “intelligent wellbore”. The ground receiving device can grasp the situation of the whole wellbore in real time, issue adjustment instructions in time, and quickly adjust the drilling rate to ensure the safe and smooth drilling operation. In addition, different types of data in collected data and information can be transmitted to the corresponding data acquisition system through the ground information transmission device, and the visualization and real-time diagnosis of the working state of the drill string network system can be implemented.

As shown in FIG. 4, the operation control system mainly consists of a processor, a mechanical arm controller, a wireless communication module and a human-machine interface. The processor is responsible for the coordination and control of the whole system, including receiving and processing sensor data, and determining how much and how far each joint should move. The mechanical arm controller is responsible for sending signals so that the driver drives the mechanical arm to a specified angle, including positioning, grabbing and launching intelligent microspheres. The wireless communication module is responsible for the data transmission between the intelligent microspheres and the drill pipe sensor. The human-machine interface is responsible for providing an interactive interface between the operator and the control system, and displaying monitoring information, operating instructions and system status.

The cooperative working process of each component is as follows.

• • 1) Initialization and preparation: the processor is started to initialize the system. The wireless communication module establishes a connection with the drill pipe sensor to ensure a normal communication. The mechanical arm controller performs self-check and calibration to ensure that the mechanical arm is on standby.
  • 2) Stopping the drill pipe: when needing to make connection, the drill pipe stops drilling, and the operator is ready to launch the intelligent microspheres.
  • 3) Launching intelligent microspheres: the processor sends launching instructions to the mechanical arm controller, and the mechanical arm controller drives the mechanical arm to grab the intelligent microsphere and move the intelligent microsphere to the specified position. The mechanical arm precisely places the intelligent microsphere at the sensor position on the drill pipe, and the sensor on the mechanical arm feeds back the position and state information to ensure that the microsphere has been correctly placed.
  • 4) Connection and testing: the wireless communication module detects the connection between the intelligent microsphere and the drill pipe sensor to confirm that the data communication is normal.
  • 5) Monitoring and adjustment: the operator monitors the whole process through the human-machine interface to check the real-time data and the system status. If there is any abnormality, the operator can send an adjustment instruction through the human-machine interface to control the mechanical arm to re-calibrate parameters, such as position, angle and velocity, or re-launch the intelligent microspheres.
  • 6) Continue drilling: After the launching is completed, the operator confirms that all systems are normal, and the drill pipe continues drilling.

In some embodiments, after Step 201 is executed, the method further includes:

• • carrying out data preprocessing on the downhole measurement data, which specifically includes:
  • using a statistical method to check whether there are missing values and/or abnormal values in the downhole measurement data;
  • if so, filling the missing values and/or abnormal values by an average filling method to obtain the downhole measurement data filled;
  • carrying out data denoising and normalization processing on the downhole measurement data filled to obtain the downhole measurement data processed.

In some embodiments, when Step 202 is executed, it may be specifically as follows.

The measurement parameters and the inversion parameters of the model are determined in view of the drilling process characteristics and gas kick characteristic parameters according to the acquired downhole measurement data. A real-time inversion interpretation model for a downhole gas kick condition is established with reference to the constructed wellbore multiphase flow forward model and the adaptive unscented Kalman filter technology. The model uses a multi-measurement point pressure, a multi-measurement point temperature, a wellhead pressure, an inlet flow and an outlet flow as measuring parameters, and carries out real-time inversion calculation on five downhole parameters, including a gas kick position, a gas kick rate, a gas front edge height, a gas slippage velocity and an invaded gas volume, so as to realize the accurate quantitative interpretation of a downhole gas kick condition of deepwater drilling. FIG. 5 is a flowchart of inversion calculation based on an adaptive unscented Kalman filter algorithm in this embodiment.

The wellbore multiphase flow forward model is as follows:

constructing three control equations of mass conservation, momentum conservation and energy conservation for a heat transfer and mass transfer process of wellbore multiphase flow resulted from a downhole gas kick condition based on a theory of wellbore multiphase flow and heat transfer, and establishing respective mathematical models for different fluid components and heat exchange areas.

In this embodiment, the mass conservation equation is specifically constructed as follows:

establishing the respective mass conservation equations of the gas phase, the drilling fluid and the cuttings according to the gas-liquid-solid three-phase flow process after gas invades the wellbore:

• • A. a gas-phase mass conservation equation:

∂ ∂ t ( ρ g ⁢ α g ⁢ A ) + ∂ ∂ z ( ρ g ⁢ α g ⁢ v g ⁢ A ) = q g ( 1 )

• • B. a liquid-phase mass conservation equation:

∂ ∂ t ( ρ l ⁢ α l ⁢ A ) + ∂ ∂ z ( ρ l ⁢ α l ⁢ v l ⁢ A ) = 0 ( 2 )

• • C. a solid-phase mass conservation equation:

∂ ∂ t ( ρ s ⁢ α s ⁢ A ) + ∂ ∂ z ( ρ s ⁢ α s ⁢ v s ⁢ A ) = q s ( 3 )

• • where t is time in unit of s; z is an axial displacement in unit of m; A is an annular flow area in unit of m²; ρ_g, ρ_l and ρ_s are density of the gas phase, density of the drilling fluid and density of the cuttings, respectively, in unit of kg/m³; α_g, α_l and α_s are volume fractions of the gas phase, the drilling fluid and the cuttings, respectively, which are dimensionless; v_g, v_l and v_s are actual flow rates of the gas phase, the drilling fluid and the cuttings, respectively, in unit of m/s; q_g is a gas kick rate per unit thickness in unit of kg/(s·m); q_s is a velocity of generating the cuttings per unit thickness in unit of kg/(s·m).

In this embodiment, the momentum conservation equation is specifically as follows:

∂ ∂ t ( ρ g ⁢ α g ⁢ v g ⁢ A + ρ l ⁢ α l ⁢ v l ⁢ A + ρ s ⁢ α s ⁢ v s ⁢ A ) + ∂ ∂ z ( ρ g ⁢ α g ⁢ v g 2 ⁢ A + ρ l ⁢ α l ⁢ v l 2 ⁢ A + 
 ρ s ⁢ α s ⁢ v s 2 ⁢ A ) + ( ρ g ⁢ α g + ρ l ⁢ α l + ρ s ⁢ α s ) ⁢ g ⁢ sin ⁢ θ ⁢ A + ∂ ( p ⁢ A ) ∂ z + A ⁢ ∂ p f ∂ z = 0 ( 4 )

where p is pressure; g is the acceleration of gravity, which is 9.81 m/s²; θ is the included angle between the borehole direction and the horizontal direction; pr is the frictional pressure drop in unit of Pa; S is the circumference of the pipe wall or the section in unit of m.

In this embodiment, the energy conservation equation is specifically as follows.

During drilling fluid circulation, the formation exchanges heat with the drilling fluid in the annulus, and the drilling fluid in the annulus exchanges heat with the drilling fluid in the drill string. The whole circulation process of the drilling fluid in the well can be regarded as a heat exchanger with certain boundary conditions. The wellbore-formation heat transfer system can be divided into five areas in the radial direction, namely, 1) the inside of the drill string; 2) the drill pipe wall; 3) the annulus; 4) the borehole wall; 5) the formation. According to a first law of thermodynamics, that is, the energy increment in the micro-element is equal to the sum of the net heat flow into the micro-element and the work done to the micro-element by the outside world. The respective control equations are established for the five divided regions, and the equations are interrelated and finally solved simultaneously.

A. The drilling fluid flows downward in the drill string. The energy increment of the fluid cell in the drill string includes the axial convective heat transfer, the radial forced convective heat transfer with the drill string wall, and the friction heat source term resulted from flow. According to the law of conservation of energy, the control equation of the temperature field of fluid in the drill string is obtained:

Q p - ρ l ⁢ q l ⁢ C l ⁢ ∂ T p ∂ z - 2 ⁢ π ⁢ r pi ⁢ h pi ( T p - T w ) = ρ l ⁢ C l ⁢ π ⁢ r ⁢ ∂ T p ∂ t . ( 5 )

B. The drill string wall is connected with the drill string and the fluid cell in the annulus. The forced convective heat transfer is generated between the inner and outer walls of the drill string, the drill string and the annulus. The drill string itself is made of steel with good thermal conductivity. Therefore, the energy increment at the drill string wall consists of heat conduction and convective heat transfer, and then the control equation of the temperature field of the drill string wall is obtained:

k w ⁢ ∂ 2 T w ∂ z 2 + 2 ⁢ r po ⁢ h po r po 2 - r pi 2 ⁢ ( T o - T w ) + 2 ⁢ r pi ⁢ h pi r po 2 - r pi 2 ⁢ ( T p - T w ) = ρ w ⁢ C w ⁢ ∂ T w ∂ t . ( 6 )

C. The drilling fluid flows upward in the annulus. The energy increment constitution of the fluid cell in the annulus is basically the same as that of the drill string unit, including the axial convective heat transfer, the radial forced convective heat transfer with the outer wall of the drill string and the borehole wall, and the viscous friction term. The fluid in the annulus is a mixture of the drilling fluid, the natural gas and the cuttings. When calculating the thermophysical parameters of the fluid in the annulus, it is necessary to consider the influence of each component and introduce volume fraction correction to obtain the control equation of the temperature field of multiphase fluid in the annulus:

Q a + ( ρ g ⁢ C g ⁢ q g ⁢ α g + ρ l ⁢ C l ⁢ q l ⁢ α l + ρ s ⁢ C s ⁢ q s ⁢ α s ) ⁢ ∂ T a ∂ z + 2 ⁢ π ⁢ r ci ⁢ h ci ( T c - T a ) + 
 2 ⁢ π ⁢ r po ⁢ h po ( T w - T a ) = ( ρ g ⁢ C g ⁢ α g + ρ l ⁢ C l ⁢ α l + ρ s ⁢ C s ⁢ α s ) ⁢ π ⁡ ( r ci 2 - r po 2 ) ⁢ ∂ T a ∂ t . ( 7 )

D. In the actual borehole, the casing and the cement sheath which are buried can be unified and simplified as the borehole wall unit. The borehole wall unit mainly exchanges heat with fluid in the annulus and the formation in a manner of the forced convective heat transfer. Taking into account the influence of axial heat conduction, the temperature control equation of the borehole wall is established:

k c ⁢ ∂ 2 T c ∂ z 2 + 2 ⁢ r co ⁢ h co r co 2 - r ci 2 ⁢ ( T f - T c ) + 2 ⁢ r ci ⁢ h ci r co 2 - r ci 2 ⁢ ( T a - T c ) = ρ c ⁢ C c ⁢ ∂ T c ∂ t ( 8 )

E. According to the above basic assumptions, only radial and axial heat conduction is taken into account in the formation unit, and the temperature control equation of the formation is obtained according to the law of conservation of energy:

∂ 2 T f ∂ z 2 + ∂ 2 T f ∂ r 2 + 1 r ⁢ ∂ T f ∂ r = ρ f ⁢ C f μ f ⁢ ∂ T f ∂ t ; ( 9 )

where ρ is a density in unit of kg/m³; Q is a viscous friction power in unit of w/m; q is the fluid flow in unit of L/s; T is the temperature in unit of K; h is the convective heat transfer coefficient in unit of W/(m²° C.); μ is a thermal conductivity in unit of W/(m° C.); c is the specific heat in unit of J/(kg·° C.); z is the axial coordinate in unit of m; r is the radial coordinate in unit of m. Subscript l denotes the liquid phase; subscript g denotes the gas phase; subscript p denotes the drill string; subscript w denotes the drill string wall; subscript a is the annulus; subscript c is the casing; subscript f is the formation; subscript pi is the inner wall of the drill string; subscript po is the outer wall of the drill string; subscript ci is the inner wall of the casing; subscript co is the outer wall of the casing.

The adaptive unscented Kalman filter algorithm is specifically implemented as follows.

The optimal estimator suitable for nonlinear time-varying noise interference is constructed after introducing an adaptive factor into the unscented Kalman filter, as shown in FIG. 5. The covariance of system noise and observation noise is corrected in real time to improve the precision and convergence of filtering calculation for real downhole data.

• • (1) The state {circumflex over (x)}₀ and the covariance P₀ are initialized. The algorithm is initialized with the error covariance (P_k) and the initial value of the state estimation x (k=0).

x ˆ 0 = E ⁡ ( x 0 ) ( 10 ) P 0 = E [ ( x 0 - x ˆ 0 ) ⁢ ( x 0 - x ˆ 0 ) T ] ( 11 )

• • (2) Sigma calculation is carried out. The Sigma point

x k - 1 / k - 1 ( i )

is calculated by the state estimation value {circumflex over (x)}_k-1/k-1 and the error covariance P_k-1/k-1 at previous moment:

x k - 1 / k - 1 ( i ) = ⁢ { x ^ k - 1 / k - 1 i = 0 x ^ k - 1 / k - 1 + ( ( n + λ ) ⁢ P k - 1 / k - 1 ) i i = 1 , … , l x ^ k - 1 / k - 1 - ( ( n + λ ) ⁢ P k - 1 / k - 1 ) i i = l + 1 , … , 2 ⁢ l ; ( 12 )

where 1 is the number of states, and the state λ is a scaling factor, and λ=α²(l+β)−l; α is a scale factor, which generally takes the value of [0,1]; β is an adjustable parameter. For Gaussian distribution, when the state vector is one-dimensional, β=3−l and β=0 is usually selected.

Each Sigma point can be obtained by a nonlinear equation:

x k / k - 1 ( i ) = f ⁡ ( x k - 1 / k - 1 ( i ) ) + q k ⁢ i = 0 , 1 , … , 2 ⁢ l ( 13 )

• • (3) The state variables (the gas kick position, the gas kick rate, the gas front edge height, the gas slippage velocity and the invaded gas volume) and the error covariance are updated. Propagating or updating a set of Sigma points by using the state equation is called

x k / k - 1 ( i ) ,

and the state mean {circumflex over (x)}_k/k-1 and the covariance matrix P_k/k-1 are further predicted.

x ˆ k / k - 1 = ∑ i = 0 2 ⁢ l W i ( m ) ⁢ x k / k - 1 ( i ) ( 14 ) P k / k - 1 = ∑ i = 0 2 ⁢ l W i ( c ) ( x k / k - 1 ( i ) - x ˆ k / k - 1 ) ⁢ ( x k / k - 1 ( i ) - x ˆ k / k - 1 ) T + Q k - 1 ( 15 )

where

W i ( m )

is the weight used for mean weighting, and

W i ( c )

is the weight used for covariance weighting. σ is a non-negative weight coefficient, and the optimal value is 2 for Gaussian prior noise.

W i ( m ) = { λ / ( n + λ ) i = 0 1 / 2 ⁢ ( n + λ ) i ≠ 0 ( 16 ) W i ( c ) = { λ / ( n + λ ) + 1 + σ - α 2 i = 0 1 / 2 ⁢ ( n + λ ) i ≠ 0 ( 17 )

• • (4) The observed variables (the multi-measurement point pressure, the multi-measurement point temperature, the wellhead pressure, the inlet flow and the outlet flow) are updated. Propagating or updating a set of Sigma points by using the measurement equation is called

y k / k - 1 ( i ) ,

so as to predict the observed value ŷ_k/k-1;

y k / k - 1 ( i ) = g ⁡ ( x k / k - 1 ( i ) ) + r k ⁢ i = 0 , 1 , … , 2 ⁢ l ; ( 18 ) y ˆ k / k - 1 = ∑ i = 0 2 ⁢ l W i ( m ) ⁢ y k / k - 1 ( i ) . ( 19 )

• • (5) The error covariance matrix and the Kalman gain matrix are updated:

P k yy = ∑ i = 0 2 ⁢ l W i ( c ) ( y k / k - 1 ( i ) - y ˆ k / k - 1 ) ⁢ ( y k / k - 1 ( i ) - y ˆ k / k - 1 ) T + R k , ( 20 ) P k x ⁢ y = ∑ i = 0 2 ⁢ l W i ( c ) ( y k / k - 1 ( i ) - y ˆ k / k - 1 ) ⁢ ( y k / k - 1 ( i ) - y ˆ k / k - 1 ) T , ( 21 ) K k = P k xy ( P k y ⁢ y ) - 1 , ( 22 ) Q k = ( 1 - Γ k ) ⁢ Q k - 1 + Γ k [ K k ⁢ ξ k ⁢ ξ k T ⁢ K k T + P k - ∑ i = 0 2 ⁢ l W i ( c ) ( x k / k - 1 ( i ) - x ˆ k / k - 1 ) ⁢ 
 ( x k / k - 1 ( i ) - x ˆ k / k - 1 ) T , ( 23 ) R k = ( 1 - Γ k ) ⁢ R k - 1 + Γ k [ ξ k ⁢ ξ k T - ∑ i = 0 2 ⁢ l W i ( c ) ( y k / k - 1 ( i ) - y ˆ k / k - 1 ) ⁢ ( y k / k - 1 ( i ) - 
 y ˆ k / k - 1 ) T ] , ( 24 ) q k = ( 1 - Γ k ) ⁢ q k - 1 + Γ k [ x ˆ k - ∑ i = 0 2 ⁢ l W i ( m ) ⁢ f ⁡ ( x k / k - 1 ( i ) ) ] , ( 25 ) r k = ( 1 - Γ k ) ⁢ r k - 1 + Γ k [ y ˆ k - ∑ i = 0 2 ⁢ l W i ( m ) ⁢ g ⁡ ( x k / k - 1 ( i ) ) ] , ( 26 ) ξ k = y k - y ^ k / k - 1 - r k ( 27 ) Γ k = 1 - η 1 - η k ⁢ ( 0 < η < 1 ) ( 28 )

• • (6) Based on the acquired new measurement value y_k, the state mean {circumflex over (x)}_k/k and the covariance matrix P_k/k at the current moment are corrected and updated to obtain an optimal covariance matrix;

x ˆ k / k = x ˆ k / k - 1 + K k ( y k - y ˆ k / k - 1 ) , ( 29 ) P k / k = P k / k - 1 - K k ⁢ P k yy ⁢ K k T . ( 30 )

The parameters of the inversion interpretation model for the downhole gas kick condition are determined as follows:

• • 1) The measurement parameters and inversion parameters are determined.

The downhole gas kick condition needs to be combined with the wellbore hydraulics model to correct the inversion parameters by using real-time measured parameters, so as to realize the overall monitoring of the downhole gas kick condition.

Combined with the adaptive unscented Kalman filter (UKF) algorithm discussed above, the state variable is the inversion parameter. Based on the analysis of the wellbore hydraulics model, it is determined that the measurement parameters closely related to the downhole gas kick condition include the multi-measurement point pressure, the multi-measurement point temperature, the wellhead pressure, the inlet flow and the outlet flow, and the inversion parameters include the gas kick position, the gas kick rate, the gas front edge height, the gas slippage velocity and the invaded gas volume.

With regard to the downhole gas kick condition, the measurement parameters and inversion parameters in the nonlinear system measurement equation and the state equation can be expressed as:

x = [ h kick , q kick , L G , v G , V G ] , ( 31 ) y = [ p m , N , T m , N , p m , c , q m , c , q m , s ] . ( 32 )

• • 3) The downhole gas kick monitoring algorithm is carried out.

The measurement parameters of the pressure and the outlet flow at different times are expressed as follows:

p m , N = [ p m , N ( t 1 ) , p m , N ( t 2 ) , … , p m , N ( t N ) ] , ( 33 ) T m , N = [ T m , N ( t 1 ) , T m , N ( t 2 ) , … , T m , N ( t N ) ] , ( 34 ) p m , c = [ p m , c ( t 1 ) , p m , c ( t 2 ) , … , p m , c ( t N ) ] , ( 35 ) q m , c = [ q m , c ( t 1 ) , q m , c ( t 2 ) , … , q m , c ( t N ) ] , ( 36 ) q m , s = [ q m , s ( t 1 ) , q m , s ( t 2 ) , … , q m , s ( t N ) ] ; ( 37 )

where p denotes a wellbore pressure, t₁, t₂, . . . , t_N denote N time points corresponding to the measurement data, T denotes temperature, q denotes a flow rate, the subscript m denotes the measurement data, the subscript N denotes a downhole measurement point N, the subscript c denotes a wellhead, and the subscript s denotes a surface outlet.

Using the gas kick position, the gas kick rate, the gas front edge height, the gas slippage velocity and the invaded gas volume as the inversion parameters of the model, the inversion parameters at different times can be expressed as:

h k ⁢ i ⁢ c ⁢ k = [ h k ⁢ i ⁢ c ⁢ k ( t 1 ) , h k ⁢ i ⁢ c ⁢ k ( t 2 ) , … , h k ⁢ i ⁢ c ⁢ k ( t N ) ] , ( 38 ) q kick = [ q kick ( t 1 ) , q kick ( t 2 ) , … , q kick ( t N ) ] , ( 39 ) L G = [ L G ( t 1 ) , L G ( t 2 ) , … , L G ( t N ) ] , ( 40 ) v G = [ v G ( t 1 ) , v G ( t 2 ) , … , v G ( t N ) ] , ( 41 ) V G = [ V G ( t 1 ) , V G ( t 2 ) , … , V G ( t N ) ] ; ( 42 )

where h denotes the gas kick position, q denotes the gas kick rate, L denotes the gas front edge height, v denotes the gas slippage velocity, V denotes the invaded gas volume, the subscripts kick and G denote the downhole gas kick condition.

The unscented Kalman filter (UKF) is a recursion algorithm that can be calculated in real time, which mainly corrects the state variables in the model in real time. The state variables are the inversion parameters. After the measurement parameters and the inversion parameters of downhole gas kick conditions are determined, the Equations (31) and (32) are substituted into the nonlinear system based on the adaptive unscented Kalman filter, that is, the Equations (13) and (18), so as to obtain the inversion interpretation model for the downhole gas kick condition:

x k / k - 1 ( i ) = f ⁡ ( x k - 1 / k - 1 ( i ) ) + q k = f ⁡ ( [ h k ⁢ i ⁢ c ⁢ k , q k ⁢ i ⁢ c ⁢ k , L G , v G , V G ] k - 1 / k - 1 ( i ) ) + 
 [ ( q h kick ) k 0 0 0 0 0 ( q q kick ) k 0 0 0 0 0 ( q L G ) k 0 0 0 0 0 ( q v G ) k 0 0 0 0 0 ( q V G ) k ] ( 43 ) y k / k - 1 ( i ) = g ⁡ ( x k / k - 1 ( i ) ) + r k = [ p m , N ( [ h kick , q kick , L G , v G , V G ] k / k - 1 ( i ) T m , N ( [ h kick , q kick , L G , v G , V G ] k / k - 1 ( i ) p m , c ⁢ ( [ h kick , q kick , L G , v G , V G ] k / k - 1 ( i ) q m , c ( [ h kick , q kick , L G , v G , V G ] k / k - 1 ( i ) q m , s ( [ h kick , q kick , L G , v G , V G ] k / k - 1 ( i ) ] + 
 [ ( v p m , N ) k 0 0 0 0 0 ( v T m , N ) k 0 0 0 0 0 ( v p m , c ) k 0 0 0 0 0 ( v q m , c ) k 0 0 0 0 0 ( v q m , s ) k ] ( 44 )

In some embodiments, Step 105 specifically includes:

five uncertain parameters such as the gas kick position, the gas kick rate, the gas front edge height, the gas slippage velocity and the invaded gas volume are dynamically inverted in real time according to the downhole measurement data based on the inversion interpretation model for the downhole gas kick condition, so as to realize accurate quantitative interpretation and analysis of the downhole gas kick condition.

After inversion, the model can also be verified and revised. The drilling data which is acquired in real time is used to verify the inversion model from two aspects of calculation real-time capability and calculation accuracy, thereby further improving the prediction accuracy and the generalization ability, and realizing rapid and accurate early warning and identification of the downhole overflow risk.

In some embodiments, the modeling method of multi-measurement point and multi-parameter inversion interpretation of the downhole gas kick condition, as shown in FIG. 6, may specifically include data analysis, data processing, construction of the wellbore multiphase flow forward model, determination of the adaptive unscented Kalman filter (AUKF) algorithm, determination of the observation parameters and the inversion parameters, construction of the inversion interpretation model for the downhole gas kick condition, and verification and revision of the model.

• • 1) The data analysis. The acquired drilling data is imported into the data platform, including a piece of wellhead pressure sensor data, a piece of downhole temperature sensor data, a piece of downhole pressure sensor data, a piece of inlet flow sensor data and a piece of outlet flow sensor data.
  • 2) The data preprocessing. Through the quality evaluation of valid data, the statistical method is used to check whether there are null values and abnormal values in the data, and an average filling method is used to process the missing values and the abnormal values, and then data denoising and normalization processing is carried out in sequence.
  • 3) The construction of the wellbore multiphase flow forward model. Three control equations of mass conservation, momentum conservation and energy conservation are constructed for a heat transfer and mass transfer process of wellbore multiphase flow resulted from a downhole gas kick condition based on a theory of wellbore multiphase flow and heat transfer, and mathematical models for different fluid components and heat exchange areas are established, respectively.
  • 4) The determination of the AUKF algorithm. The adaptive unscented Kalman filter algorithm is studied. The optimal estimator suitable for nonlinear time-varying noise interference is constructed after introducing an adaptive factor into the unscented Kalman filter, as shown in FIG. 5. The covariance of system noise and observation noise is corrected in real time to improve the precision and convergence of filtering calculation for real downhole data.
  • 5) The determination of the observation parameters and the inversion parameters. Different parameters are correlated with each other based on fluid mechanics and seepage mechanics. After drilling overflow, it is mainly shown in the change of the pressure and the flow rate. Therefore, in the acquisition of measurement parameters, the measurement parameters of the flow rate and the pressure are mainly taken into account. In the acquisition of inversion parameters, it is mainly taken into account under the downhole gas kick condition whether the law of overflow flow in the wellbore can be accurately described. According to the drilling process characteristics and overflow characteristic parameters, the measurement parameters are determined as the multi-measurement point pressure, the multi-measurement point temperature, the wellhead pressure, the inlet flow and the outlet flow, and the inversion parameters are determined as five downhole uncertain parameters such as the gas kick position, the gas kick rate, the gas front edge height, the gas slippage velocity and the invaded gas volume.
  • 6) The construction of the inversion interpretation model for the downhole gas kick condition. According to the constructed wellbore multiphase flow forward model, by combining the mud logging data and the downhole measurement data, an inversion interpretation model for a downhole gas kick condition based on an adaptive unscented Kalman filter (AUKF) is established. The specific construction process is shown in the Equations (31) to (44). Five uncertain parameters such as the gas kick position, the gas kick rate, the gas front edge height, the gas slippage velocity and the invaded gas volume are dynamically inverted in real time, so as to realize accurate quantitative interpretation and analysis of the downhole gas kick condition.
  • 7) The verification and revision of the model. The drilling data which is acquired in real time is used to verify the inversion model from two aspects of calculation real-time capability and calculation accuracy, thereby further improving the prediction accuracy and the generalization ability, and realizing rapid and accurate early warning and identification of the downhole overflow risk.

In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal, the internal structure diagram of which may be as shown in FIG. 7. The computer device includes a processor, a memory, an Input/Output interface (I/O for short) and a communication interface. The processor, the memory and the input/output interface are connected through a system bus, and the communication interface is connected to the system bus through the input/output interface. The processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is configured to store inversion parameters. The input/output interface of the computer device is configured to exchange information between the processor and the external device. The communication interface of the computer device is configured to be communicated with an external terminal through network connection. The computer program, when executed by the processor, implements a real-time inversion method of multi-measurement point and multi-parameter drilling.

It can be understood by those skilled in the art that the structure shown in FIG. 7 is only a block diagram of a part of the structure related to the scheme of the present disclosure, and does not constitute a limitation on the computer device to which the scheme of the present disclosure is applied. The specific computer device may include more or less components than those shown in the figure, or combine some components, or have different component arrangements.

In an exemplary embodiment, a computer device is further provided, which includes a memory and a processor, where a computer program is stored in the memory, and the processor, when executing the computer program, implements the steps in the above method embodiments.

In an exemplary embodiment, a computer-readable storage medium is provided, on which a computer program is stored, where the computer program, when executed by a processor, implements the steps in the above method embodiments.

In an exemplary embodiment, a computer program product is provided, including a computer program, where the computer program, when executed by a processor, implements the steps in the above method embodiments.

It should be noted that the user information (including but not limited to user equipment information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in the present disclosure are all information and data authorized by users or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

Those skilled in the art can understand that all or part of the processes in the method of implementing the above embodiment can be completed by instructing related hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, the computer program can include the processes of the embodiments of the above method. Any reference to the memory, the database or other media used in various embodiments provided by the present disclosure may include at least one of a non-volatile memory and a volatile memory. The non-volatile memory may include a Read-Only Memory (ROM), a magnetic tape, a floppy disk, a flash memory, an optical memory, a high-density embedded non-volatile memory, a Resistive Random Access Memory (ReRAM), a Magneto-Resistive Random Access Memory (MRAM), a Ferroelectric Random Access Memory (FRAM), a Phase Change Memory (PCM), a graphene memory, etc. The volatile memory may include a Random Access Memory (RAM) or an external cache memory. By way of illustration and not limitation, RAM can be in various forms, such as a Static Random Access Memory (SRAM) or a Dynamic Random Access Memory (DRAM).

The databases involved in various embodiments provided by the present disclosure may include at least one of a relational database and a non-relational database. The non-relational database may include, but is not limited to, a distributed database based on a blockchain. The processors involved in the embodiments according to the present disclosure can be, but are not limited to, general processors, central processing units, graphics processors, digital signal processors, programmable logics, data processing logics based on quantum computing, etc.

To sum up, the present disclosure has the following technical effects.

• • (1) The method described in the present disclosure applies a real-time measurement and transmission device along a drill pipe based on intelligent microspheres, uses a conventional drill string combined with an improved measurement and transmission tool, and arranges a plurality of wireless sensors at the same interval on the drill string along the longitudinal direction, so that various state parameters at different positions of the wellbore in key well sections can be monitored at the same time, thereby forming a distributed sensor network, and implementing multi-point real-time measurement of engineering parameters such as temperature and pressure, so as to establish a complete view of a certain drilling parameter along the drill string, and make up for the defect in the prior art that the parameters are only measured in a single place at a single time. The present disclosure can be further applied to sensors such as dielectric constants and optical probes to realize the measurement of different parameters.
  • (2) The method described in the present disclosure uses the adaptive unscented Kalman filter technology and the wellbore multiphase flow forward model. A real-time inversion interpretation model for a downhole gas kick condition is established with reference to the constructed wellbore multiphase flow forward model and the adaptive unscented Kalman filter technology based on the drilling data acquired above. The model uses a multi-measurement point pressure, a multi-measurement point temperature, a wellhead pressure, an inlet flow and an outlet flow as measuring parameters, and carries out real-time inversion calculation on five downhole parameters, including a gas kick position, a gas kick rate, a gas front edge height, a gas slippage velocity and an invaded gas volume, so as to realize the accurate quantitative interpretation of a downhole gas kick condition of deep drilling. The present disclosure can be further effectively applied to the working conditions such as lost circulation, co-existence of the loss and the kicks, and stuck pipe.

The technical features of the above embodiments can be combined at will. In order to make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction between the combinations of these technical features, the combinations should be considered as the scope recorded in this specification.

In the present disclosure, specific examples are applied to illustrate the principle and implementation of the present disclosure, and the explanations of the above embodiments are only used to help understand the method and core ideas of the present disclosure. At the same time, according to the idea of the present disclosure, there will be some changes in the detailed description and application scope for those skilled in the art. To sum up, the contents of the specification should not be construed as limiting the present disclosure.