Drilling fluid leakage layer positioning method

Abstract

The invention relates to a drilling fluid leakage layer positioning method. The method comprises the steps that monitoring temperature data of a plurality of monitoring points with different depths set in a shaft are collected; the method comprises the following steps: preliminarily determining a potential leakage layer position of a drilling fluid leakage layer according to monitoring temperature data of a plurality of monitoring points with different depths on the basis of gradient change of a temperature value along with the depth of a shaft, setting a search range for further refining the leakage layer position according to the potential leakage layer position, and setting an initial search position of search; the initial search position is used as a search starting point, and at least two dynamic response indexes of temperature changing along with time are calculated; the search starting point is updated in a set mode, and iteration is executed again until iteration reaches a stop condition; and comparing the dynamic response indexes of at least two temperature changes along with time, which are obtained by each iteration, and based on a set strategy, determining a fine leakage layer position assumed by one iteration as a final drilling fluid leakage layer positioning position.

Description

Technical Field

[0001] This invention relates to the field of oil and gas drilling technology, and in particular to a method for locating drilling fluid leakage zones. Background Technology

[0002] With the continued growth of global energy demand and the gradual depletion of shallow oil and gas resources, oil and gas exploration and development are constantly moving towards deeper and ultra-deeper layers.

[0003] Loss of circulating drilling fluid has become one of the most common and challenging complex problems in deep and ultra-deep well drilling. Loss not only causes significant loss of drilling fluid but can also trigger complex accidents such as stuck pipe, blowouts, and wellbore collapse, seriously threatening downhole safety and drilling progress. Therefore, accurately identifying the location of the leakage zone is a crucial step in implementing effective plugging and ensuring well control safety.

[0004] Existing technologies commonly used for identifying leaky layers include empirical judgment and riser pressure methods. Due to limitations in personnel experience or sensor technology, these techniques generally suffer from low positioning accuracy, slow response, and susceptibility to interference from operating conditions, making it difficult to meet the real-time identification requirements under complex well conditions. Summary of the Invention

[0005] To address the aforementioned problems, the present invention aims to provide a drilling fluid leakage zone location method that combines a wellbore-formation transient heat transfer model with distributed fiber optic temperature sensing data to overcome the shortcomings of existing technologies that rely on experience-based judgment or single-point measurement, resulting in low location accuracy, delayed response, and inability to achieve real-time identification.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: In a first aspect, this application provides a method for locating a drilling fluid lost circulation zone, comprising: Step (1): Collect monitoring temperature data from multiple monitoring points at different depths set in the wellbore; Step (2): Based on the monitoring temperature data of multiple monitoring points at different depths, and based on the gradient change of temperature values ​​with well depth, the potential location of the drilling fluid leakage layer is initially determined. Step (3): Based on the potential location of the leaking layer, set the search range for further refined locations of the leaking layer, and set the initial search location; Step (4): Using the initial search position as the search starting point, perform the following iterative processing: Taking the current iteration's search starting point as the fine leak location, and based on this, using the preset wellbore-formation transient heat transfer model, the calculated data of temperature changes over time at at least two reference points in the wellbore are simulated. Obtain measured data on temperature changes over time at at least two reference points; Calculate at least two dynamic response indices for temperature changes over time based on calculated and measured data corresponding to at least two reference points. Update the search starting point in the set manner and re-execute the iteration until the iteration reaches the stopping condition; Step (5): Compare at least two dynamic response indices of temperature change over time obtained in each iteration, and determine the fine leakage layer location assumed in one of the iterations based on the set strategy, as the final drilling fluid leakage layer location.

[0007] In one implementation, in step (1), monitoring temperature data of multiple monitoring points at different depths set in the wellbore are collected by a distributed optical fiber temperature sensor. In step (4), a distributed fiber optic temperature sensor or a fixed temperature sensor is used to obtain measured data of temperature changes over time at at least two reference points.

[0008] In one implementation, in step (2), several sample points are selected from multiple monitoring points according to a set depth interval, and based on the monitoring temperature data of several sample points, the first derivative of the temperature value with respect to the depth is calculated, and the sample point position where the first derivative changes with the sample point number beyond a set threshold is selected as the potential leak layer position.

[0009] In one implementation, in step (3), a set number of sample points are selected above and below the potential leak location, and the search range between the two sample points with the largest distance difference is used as the search range for fine search. The initial search position is the center of the search area, or the upper or lower endpoints.

[0010] In one implementation, in step (4), the two reference points are the wellhead and the bottom of the wellbore.

[0011] In one implementation, updating the search starting point includes: using a fixed search step size, superimposing the search starting point relative to the previous iteration to obtain a deeper position as the search starting point for the next iteration.

[0012] In one implementation, the iteration reaches a stopping condition, including: completing the iterative processing of the entire search range with a fixed search step size.

[0013] The present invention has the following advantages due to the adoption of the above technical solutions: This invention can quickly respond to the occurrence of leakage and accurately determine the location of the leakage layer under complex well conditions, providing real-time and reliable leakage layer identification information for oil and gas drilling operations, significantly improving the speed and accuracy of leakage layer identification; at the same time, the method has a reasonable structure and is easy to integrate with existing downhole monitoring systems, with good practicality and promotion value, which can effectively support downhole safety operations and leakage management, and reduce drilling risks and operating costs. Attached Figure Description

[0014] Figure 1 This is a schematic diagram of the simulation calculation process of the wellbore-formation transient heat transfer model in one embodiment of this application; Figure 2 This is a schematic diagram of index calculation for iterative processing according to an embodiment of this application. Detailed Implementation

[0015] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the described embodiments of the present invention are within the scope of protection of the present invention.

[0016] based on Figure 1 and Figure 2 In one embodiment, a drilling fluid lost zone location method of this application is described.

[0017] Specifically, the method in this application embodiment includes the following steps: Step (1): Collect monitoring temperature data from multiple monitoring points at different depths set in the wellbore; Specifically, temperature data can be collected from multiple monitoring points at different depths within the wellbore using distributed fiber optic temperature sensors.

[0018] Step (2): Based on the monitoring temperature data of multiple monitoring points at different depths, and based on the gradient change of temperature values ​​with well depth, the potential location of the drilling fluid leakage layer is initially determined. Specifically, several sample points are selected from multiple monitoring points according to a set depth interval. Based on the monitoring temperature data of several sample points, the first derivative of the temperature value with respect to depth is calculated. The sample point locations where the first derivative changes with the sample point number beyond a set threshold are selected as potential leak locations.

[0019] For example, wellbore temperature data is divided into multiple sampling points according to depth intervals (e.g., one sampling point every 30 meters). The gradient change between adjacent depths is calculated for the temperature value of each sampling point, i.e., the first derivative of temperature with respect to depth. By analyzing the calculated first derivative sequence, locations with significant jumps or discontinuities in the derivative values ​​are identified, and these significant abrupt changes are marked as potential leaky layers. Further analysis is performed on the actual temperature data near these abrupt changes, such as comparing the magnitude of temperature changes over a short period. If a rapid increase or decrease in temperature occurs near the abrupt change point, it is preliminarily determined that a leaky layer exists at that location.

[0020] Step (3): Based on the potential location of the leaking layer, set the search range for further refined locations of the leaking layer, and set the initial search location; Specifically, a set number (1~2) of sample points are selected above and below the potential leak location, and the search range between the two sample points with the largest distance difference is used for fine-grained search.

[0021] The initial search position is the center of the search area, or the upper or lower endpoints.

[0022] Step (4): Using the initial search position as the search starting point, perform the following iterative processing: Specifically, it includes the following detailed steps: (4-1) Taking the search starting point of the current iteration as the fine leakage layer location, and based on this, the calculated data of temperature change over time at least two reference points of the wellbore are obtained by simulating based on the preset wellbore-formation transient heat transfer model. Figure 1 The transient heat transfer model between the wellbore and the formation is illustrated.

[0023] The model input mainly includes three categories of parameters: (1) engineering parameters, such as well depth, drilling fluid discharge, circulation time, surface temperature, wellhead / bottom hole temperature and measured DTS wellbore profile temperature; (2) wellbore structure and material parameters, including the size of the drill string and casing at each level, well diameter, cement sheath thickness and its corresponding density, specific heat and thermal conductivity; (3) formation thermal property parameters, such as geothermal gradient, formation thermal conductivity, heat capacity and density. The main output of the model is the temperature distribution at different time and depth nodes, including the wellbore center temperature, annular temperature and wellbore-formation interface temperature, which can be further used to identify local temperature gradient abrupt changes caused by leakage and to invert the location of the leakage layer.

[0024] In terms of computational process, the model first constructs a radial-axial computational grid across multiple regions of the wellbore and formation based on the input parameters. Then, according to the partial differential form of the governing equations, it is discretized using a fully implicit finite difference method, transforming the transient heat conduction equations into a large-scale sparse algebraic equation system. By uniformly representing the nodal temperatures at the same time step as a matrix, and combining the wellhead, bottom-hole, and formation boundary conditions, Gauss-Seidel iteration is used to solve the problem, iteratively obtaining the temperature field across the entire time domain. During this process, grid density, time step size, and error convergence criteria are key factors determining computational accuracy and convergence stability, requiring a trade-off between accuracy and computational complexity. The resulting temperature distribution can not only be used as input for the lost-loop layer location algorithm but also provide high-quality simulation data support for subsequent machine learning-based lost-loop pattern recognition and parameter prediction.

[0025] Specific wellbore-formation transient heat transfer models can include drill string internal fluid heat transfer models, drill string wall heat transfer models, annular fluid heat transfer models, and casing, cement sheath, and formation heat transfer models.

[0026] The details of each part are described below: (a) Fluid heat transfer model inside the drill string Combination Figure 1 It can be seen that the heat of the drilling fluid micro-unit within the drill string mainly consists of the following four parts: (1) the change in the internal energy of the drilling fluid within the drill string; (2) the net heat carried by the axial upward flow of the drilling fluid downward; (3) the heat generated by convective heat transfer between the drilling fluid and the drill string wall; and (4) the work done on the drilling fluid by external forces. The following relationship can be obtained:

[0027] In the formula T 1. T 2 represents the temperature of the drilling fluid inside the drill string and the drill string wall, in °C; ρ f Drilling fluid density, kg / m³; v 1 represents the drilling fluid inlet velocity, in m / s; C pf Specific heat capacity of drilling fluid in drill string, J / kg / ℃; r 1 represents the inner diameter of the drill string, in meters (m). h 1 represents the convective heat transfer coefficient of the drill string inner wall, in W / m² / ℃; S 1 represents the heat source term, W / m 3 .

[0028] (b) Heat transfer model of drill string wall The temperature of the drill string wall is closely related to the flow of drilling fluid within the drill string and annulus. The heat of its micro-unit mainly consists of three parts: (1) heat generated by axial heat conduction through the drill string; (2) heat exchanged radially between the drill string and the drilling fluid in the annulus; and (3) the change in the unit's internal energy per unit time. The following relationship can be obtained:

[0029] In the formula T 3 represents the drilling fluid annulus temperature, in °C. ρ 2 represents the drill string density, kg / m³; C p2 The specific heat capacity of the drill string is J / kg / ℃; r 2 represents the outer diameter of the drill string, in meters (m). λ 2 represents the thermal conductivity of the drill string, in W / m / ℃; h 2 represents the convective heat transfer coefficient of the drill string outer wall, in W / m² / ℃. (c) Annular fluid heat transfer model Within the annulus, drilling fluid flows upward from the bottom of the well. In the section of the well below the lost circulation point, drilling fluid flows normally within the annulus.

[0030] When leakage occurs, the drilling fluid flows at a rate of v 3. Through the leakage zone, some fluid seeps into the formation, causing the annular velocity above the leakage point to decrease to [a certain value]. v 3-Δ v The reduced flow rate significantly alters the heat transfer characteristics of the system above the leakage point, resulting in a marked difference in annular temperature distribution under leakage conditions compared to normal conditions.

[0031] The factors affecting the heat of the drilling fluid in the annulus below the lost circulation zone mainly include four parts: (1) the net heat carried by the drilling fluid flowing upward along the wellbore axially; (2) the heat generated by convective heat transfer with the drill string wall and casing wall; (3) the change in the internal energy of the unit of drilling fluid per unit time in the annulus; and (4) the work done on the drilling fluid by external forces. The heat transfer process can be represented as:

[0032] In the formula ρ 3 represents the density of the annular drilling fluid, kg / m³; T 4 represents the temperature of the first layer of bushing, in °C; C p3 Specific heat capacity of annular drilling fluid, J / kg / ℃; r 3 represents the inner diameter of the first layer of casing, in meters (m). v 3 represents the flow velocity of the drilling fluid in the annulus, in m / s; h 3 represents the convective heat transfer coefficient between the annular drilling fluid and the first casing layer, in W / m² / ℃. S3 represents the heat source term within the annulus below the leakage point, in W / m³. 3 .

[0033] After leakage occurs, the drilling fluid flow rate in the annulus above the leakage layer decreases, and the heat transfer process can be represented as follows:

[0034] In the formula Δ v Leakage velocity, in m / s; S 3l The heat source term within the annulus above the leakage point, in W / m³.

[0035] (d) Heat transfer model of casing, cement sheath and formation The heat transfer process of the casing, cement sheath, and formation is modeled in cylindrical coordinates, considering the changes in the energy of the micro-element due to radial and axial heat conduction. For the formation without leakage, as well as the casing and cement sheath, the heat transfer model can be expressed as:

[0036] In the formula: the subscript m represents the m-th layer of medium; T m Temperature of the cement sheath, casing, or formation, in °C; r m Let be the distance (m) between the infinitesimal element and the central axis of the wellbore.

[0037] For formations with leakage, the impact of convective heat transfer generated by formation fluids on formation temperature needs to be considered. The heat transfer model can be expressed as:

[0038] subscript in formula e f Indicates an effective layer; v l This indicates the velocity of drilling fluid flowing through the formation, in m / s.

[0039] Formulas for calculating the combined thermophysical parameters of formation fluid and formation when leaking fluid flows in the formation. [21-22] for:

[0040] In the formula φ Effective porosity of rock; subscript l Indicates liquid; subscript s It indicates a solid.

[0041] To solve the multiple partial differential equations involved in the wellbore-formation transient heat transfer model, a fully implicit finite difference method (FDM) is used to discretize the governing equations in both time and space. Discretization is performed radially from the wellbore center to the formation and axially from the wellhead to the bottom of the well. Temperature terms at nodes with the same time and space are integrated to form a set of algebraic equations to be solved. The discrete equation at any node can be expressed as:

[0042] In the formula, A, B, C, D, and E are the control equation coefficients of the unit control volume. nt Indicates a time point. i , j These are the spatial nodes in the radial and axial directions of the wellbore, respectively. Through the above discretization process, the partial differential equations can be uniformly expressed in matrix form. Combined with initial and boundary conditions, the Gauss-Seidel iterative method is used to solve the problem, thereby obtaining the temperature distribution of the nodes at various times and depths.

[0043] (4-2) Obtain measured data on the temperature change over time at at least two reference points; Two reference points, specifically the wellhead and the bottom of the well.

[0044] Temperature data can be collected using distributed fiber optic temperature sensors or fixed sensors.

[0045] (4-3) Calculate at least two dynamic response indices of temperature change over time based on the calculated and measured data corresponding to at least two reference points; Dynamic response metrics can include the mean absolute error (MAE) between simulation results and measured temperatures to measure the consistency of dynamic temperature evolution.

[0046] The dynamic response index can be calculated by averaging the absolute value of the gradient change of the simulated temperature at adjacent time steps and summing them over the entire time domain to obtain the total curvature of the time dynamic response.

[0047] Different dynamic response indicators are used to construct a weighted composite indicator with dual constraints based on different reference points.

[0048] by Figure 2 For example: Numerical simulations were performed using a wellbore-formation transient heat transfer model under different assumed leakage depths to obtain the dynamic changes of wellhead and bottomhole temperatures over time within the corresponding time intervals. and .

[0049] Calculate the dynamic temperature difference index: for each assumed leak depth m The mean absolute error (MAE) between the simulation results and the measured temperature is calculated to measure the consistency of the dynamic temperature evolution process.

[0050]

[0051] Relying solely on the temperature fluctuations at the wellhead and bottom hole is insufficient to effectively distinguish differences at adjacent grid depths. Therefore, temperature gradient variation is introduced as a discriminant constraint. The absolute value of the gradient change of the simulated temperature at adjacent time steps is calculated and accumulated over the entire time domain to obtain the total curvature at the corresponding depth.

[0052] Constructing a weighted composite index with dual constraints: Considering that bottomhole temperature is more sensitive to changes in the leakage formation, while wellhead temperature is affected by both formation background temperature and inlet temperature, and its variation is relatively smaller, a well depth weight is further introduced into the similarity calculation: w top =0.8, w bot = 0.2 By combining the dynamic consistency of temperature (MAE) and total curvature (C), a multi-constraint weighted similarity function is constructed:

[0053] Where the weight coefficients satisfy The overall weighting is configured as follows:

[0054] This weighting highlights the dominant role of bottom hole temperature in leak detection and strengthens its sensitive response to thermal disturbances in the leak zone, thereby enhancing the contribution of bottom hole temperature in the final leak determination.

[0055] Step (5): Compare at least two dynamic response indices of temperature change over time obtained in each iteration, and determine the fine leakage layer location assumed in one of the iterations based on the set strategy, as the final drilling fluid leakage layer location.

[0056] Based on the weighted composite index of the dual constraints in each iteration, the lowest value corresponds to the assumed fine-grained leak location, which is used as the final location.

[0057] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here. In the embodiments provided by this invention, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection between devices or units through some interfaces, and may be electrical, mechanical, or other forms.

[0058] The integrated units implemented as software functional units described above can be stored in a computer-readable storage medium. These software functional units, stored in a storage medium, include several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute some steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0059] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for locating lost drilling fluid zones, characterized in that, include: Step (1): Collect monitoring temperature data from multiple monitoring points at different depths set in the wellbore; Step (2): Based on the monitoring temperature data of multiple monitoring points at different depths, and based on the gradient change of temperature values ​​with well depth, the potential location of the drilling fluid leakage layer is initially determined. Step (3): Based on the potential location of the leaking layer, set the search range for further refined locations of the leaking layer, and set the initial search location; Step (4): Using the initial search position as the search starting point, perform the following iterative processing: Taking the current iteration's search starting point as the fine leak location, and based on this, using the preset wellbore-formation transient heat transfer model, the calculated data of temperature changes over time at at least two reference points in the wellbore are simulated. Obtain measured data on temperature changes over time at at least two reference points; Calculate at least two dynamic response indices for temperature changes over time based on calculated and measured data corresponding to at least two reference points. Update the search starting point in the set manner and re-execute the iteration until the iteration reaches the stopping condition; Step (5): Compare at least two dynamic response indices of temperature change over time obtained in each iteration, and determine the fine leakage layer location assumed in one of the iterations based on the set strategy, as the final drilling fluid leakage layer location.

2. The method according to claim 1, characterized in that, In step (1), monitoring temperature data of multiple monitoring points at different depths set in the wellbore are collected by distributed fiber optic temperature sensors. In step (4), a distributed fiber optic temperature sensor or a fixed temperature sensor is used to obtain measured data of temperature changes over time at at least two reference points.

3. The method according to claim 1, characterized in that, In step (2), several sample points are selected from multiple monitoring points according to a set depth interval, and the first derivative of the temperature value with respect to the depth is calculated based on the monitoring temperature data of several sample points. The sample point location where the first derivative changes with the sample point number beyond a set threshold is selected as the potential leak location.

4. The method according to claim 3, characterized in that, In step (3), a set number of sample points are selected above and below the potential leak location as the center, and the search range between the two sample points with the largest distance difference is used for fine search. The initial search position is the center of the search area, or the upper or lower endpoints.

5. The method according to claim 4, characterized in that, In step (4), the two reference points are the wellhead and the bottom of the well.

6. The method according to claim 4, characterized in that, The methods for updating the search starting point include: using a fixed search step size, superimposing the search starting point relative to the previous iteration to obtain a deeper position as the search starting point for the next iteration.

7. The method according to claim 6, characterized in that, The iteration stops when the following conditions are met: the iteration process is completed for the entire search range with a fixed search step size.

8. A computer storage medium, characterized in that, The device contains a computer program, which is executed by a processor to implement the method of any one of claims 1 to 7.

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