Method for Real-time Quantification of Boundary Distance in Geological Steering Calculation for Horizontal Wells

Abstract

The present invention relates to the technical field of oilfield development, and is a method for real-time quantification of the boundary distance in horizontal well geological steering calculation. It includes establishing a horizontal well formation model and a highly deviated well formation model, and numerically simulating and calculating to convert the model parameter library into a logging response library of the logging-while-drilling instrument; according to the relationship between the logging response value of the instrument in the response library and the dip angle, etc., constructing a functional relationship between the model parameters and the logging apparent value; obtaining logging data in real time at the well site, and solving the system of equations established according to the functional mapping relationship to obtain the distance information between the wellbore and the formation boundary in real time; numerically simulating and calculating the response values corresponding to the model parameters, and outputting the inversion result. The present invention uses the method of constructing an analytical function to correlate the logging response with the model parameters, and obtains the formation dip angle, boundary distance, surrounding rock resistivity, etc. by numerically solving the system of nonlinear equations. The analytical method has higher calculation accuracy, and the calculation result does not depend on the initial model, avoiding a great influence of human factors on the calculation result.

Description

Technical Field

[0001] The present invention relates to the technical field of oilfield development, and is a method for real-time quantification of calculating boundary distances in horizontal well geosteering. Background Art

[0002] With the development of the petroleum industry and technological progress, highly deviated wells and horizontal wells have become the main well types for oil and gas production. During the drilling process of horizontal wells, it is crucial to optimize the position of the wellbore in the reservoir. The information on the position of the wellbore in the reservoir in a horizontal well needs to be obtained relying on logging information. Since the measurement by the logging-while-drilling method has real-time property and can timely obtain the logging responses of the drilled formations, providing guidance for wellbore trajectory optimization and bit adjustment, logging-while-drilling has been widely applied in the geosteering of horizontal wells and highly deviated wells.

[0003] The electromagnetic wave resistivity tool while drilling has a large detection depth, rich logging curves, and can improve resistivity measurements at multiple depths simultaneously, becoming the most widely used resistivity logging-while-drilling tool in the current market. This type of tool has two or more source distances and multiple electromagnetic wave signal emission frequencies for resistivity measurement, and combines azimuthal imaging data for geosteering applications, and has achieved good results at present.

[0004] For the geosteering application of azimuthal boundary detection tools, the inversion method is usually adopted to calculate formation parameters. However, the common inversion methods in the current market are the fast inversion method and the non-linear iterative inversion method. During real-time geosteering, due to the small number of uploaded curves, the real-time inversion is quite difficult. At the same time, since inversion is a method for solving local optimal solutions, in the case of a small number of uploaded curves, it will inevitably face the problems of local optimality and multi-solution, resulting in an increase in the uncertainty of the real-time steering inversion parameters based on azimuthal boundary detection tools, a strong dependence on the initial values, and a large amount of associated information in the initial value modeling. All these increase the application difficulty of the geosteering method based on the inversion theory and also limit the popularization of the geosteering technology of azimuthal boundary detection tools. In China, the understanding of the inversion geosteering method for azimuthal boundary detection tools is vague, and still relies on the services of foreign service companies. In the case that foreign service companies do not understand geological information as well as domestic personnel, there are generally situations of low penetration rate and unreasonable well location layout. Summary of the Invention

[0005] The present invention provides a method for real-time quantification of calculating boundary distances in horizontal well geosteering, overcoming the above-mentioned deficiencies of the prior art. It analyzes formation parameters based on logging responses under the condition of no initial model, eliminating the problem that the result falls into a local optimal solution, and at the same time making the information relied on for solving formation parameters less and the steering application more flexible.

[0006] The technical solution of the present invention is realized by the following measures: A method for real-time quantification of calculating boundary distances in horizontal well geosteering, comprising the following steps:

[0007] Step 1: Establish a formation model library including a horizontal well formation model and a highly deviated well formation model. The model parameters in the formation model library include four types of parameters: dip angle, boundary distance, resistivity of surrounding rock, and formation resistivity. Numerical simulation calculates to convert the formation model library parameters into a logging response library for the logging-while-drilling instrument. The response library includes four resistivity curves with different detection depths, a direction signal strength curve, and a curve of the azimuth where the high side of the direction signal is located.

[0008] Step 2: Construct a functional relationship between the model parameters and the logging apparent values according to the relationship between the logging response values of the instrument in the response library and the dip angle, boundary distance, formation resistivity, and resistivity of surrounding rock.

[0009] Step 3: Obtain logging data in real time at the well site, including four apparent resistivity curves, a direction signal strength curve, and a curve of the azimuth where the high side of the direction signal is located. Establish a non-linear equation set of formation parameters - measurement response according to the functional mapping relationship.

[0010] Step 4: Solve the non-linear equation set, calculate the parameters of the dip angle, boundary distance, formation resistivity, and resistivity of surrounding rock, and obtain the distance information of the wellbore from the formation boundary in real time.

[0011] Step 5: Numerically simulate and calculate the response values corresponding to the model parameters, match them with the measured curves, obtain the quality control curve, and output the inversion result.

[0012] The following is a further optimization and / or improvement of the above technical solution of the invention:

[0013] Preferably, the above Step 1 includes:

[0014] Step 11: Establish a horizontal well formation model and a highly deviated well formation model. The model parameters include dip angle, boundary distance, resistivity of surrounding rock, and formation resistivity (i.e., resistivity of the target layer). Among them: the dip angle is the angle between the wellbore axis direction and the formation normal direction; the boundary distance is the distance in the radial direction from the midpoint of the electromagnetic wave resistivity logging-while-drilling instrument to the boundary; the resistivity of surrounding rock is the resistivity of the formation on the non-instrument side of the layer interface closest to the midpoint of the instrument; the resistivity of the target layer refers to the resistivity of the layer where the midpoint of the instrument is located.

[0015] Step 12: Numerically simulate and calculate to convert the model parameter library into a logging response library for the logging-while-drilling instrument. The simulation tool is for a multi-depth electromagnetic wave resistivity instrument and has four resistivity curves of measurement depths, provides a direction signal strength curve and a direction signal high side azimuth signal.

[0016] Preferably, the above Step 2 includes:

[0017] Step 21: Examine the corresponding relationships among dip angle, boundary distance, formation resistivity, surrounding rock resistivity, and logging response, analyze the variation laws they satisfy, and determine the functional relationship among dip angle, boundary distance, formation resistivity, surrounding rock resistivity, and logging response through semi - quantitative analysis;

[0018] Step 22: Use curve fitting to extract characteristic curves, combine the functional relationship between model parameters and simulated responses, determine the mapping relationship between simulated responses and formation parameters (i.e., integral operator), and complete the construction of the functional relationship between model parameters and simulated responses;

[0019] The resistivity curve satisfies the following functional relationship:

[0020]

[0021] Where Ra is the apparent resistivity, DTB is the distance to the nearest boundary, Rs is the resistivity of the surrounding rock of the nearest boundary, Rt is the resistivity of the target formation, Dip is the relative dip angle between the borehole and the formation, and f is the undetermined mapping relationship;

[0022] The direction signal curve satisfies the following functional relationship:

[0023]

[0024] Where, signal str ,sigal amz are the geological signal intensity and the high - side azimuth of the geological signal respectively, DTB is the distance to the nearest boundary, Rs is the resistivity of the surrounding rock of the nearest boundary, Rt is the resistivity of the target formation, Dip is the relative dip angle between the borehole and the formation, and g, h are the undetermined mapping relationships;

[0025] After determining the expression of formula (2), fix the resistivity of the surrounding rock and the resistivity of the target formation, change the relative dip angle and the boundary distance, sort the boundary distances and relative dip angles that make the equation s(signal str ,sigal amz )=(signal str ,sigal amz ) hold from small to large to form a two - dimensional array, use the boundary distance and relative dip angle as independent variables and (signal str ,sigal amz ) as the dependent variable to fit a regression equation, and record the equation expression as g(DTB) - h(Dip);

[0026] Sort the equation coefficients in ascending order according to the priority order of apparent resistivity, direction signal intensity, and direction signal azimuth, and establish an equation coefficient library;

[0027] According to the equation coefficients and response values, the equation corresponding to the coefficient library satisfies:

[0028]

[0029] Wherein, Ra1, Ra2, Ra3 and Ra4 represent the apparent resistivity curves of four different detection depths.

[0030] Preferably, the above step 3 includes:

[0031] Step 31: The well site acquires logging data in real time, including four apparent resistivity curves at different detection depths, a directional signal strength curve, and a directional signal high side position curve;

[0032] Step 32: Substitute the measured values into the functional mapping relationship satisfied by the corresponding curves in turn, establish an equation that relates the measured response to the function, and the equation relationship established by multiple resistivity and direction signals forms a formation parameter-measurement response nonlinear equation group;

[0033] Select four resistivity curves for real-time upload. The resistivity curves are counted as Ra1, Ra2, Ra3, and Ra4, and one direction signal strength curve is signal. str 、A direction signal high side azimuth curve Signal amz ,

[0034] According to formula (3), the functional relationship between the response and the model is established:

[0035]

[0036]

[0037]

[0038]

[0039]

[0040]

[0041] Step 33: Move the terms on the right side of the six equations to the left side of the equations, and combine the equations to form six nonlinear equations:

[0042]

[0043] Preferably, in step 4, the six equations of formula (10) are transformed into a problem of solving six unknown quantities, that is, the set of equations to be solved is:

[0044] H i (x1, x2, x3, x4, x5, x6) = 0i = 1, 2, ..., 6 (11)

[0045] The unknown quantity to be solved is:

[0046] X = (x1, x2, x3, x4, x5, x6) T

[0047] H i is the i-th of six equations, where x1, x2, x3, x4, x5, x6 are the unknowns to be solved, representing dip angle, upper and lower boundary distances, resistivity of the target layer, resistivity of the upper and lower surrounding rocks respectively;

[0048] In the formula:

[0049]

[0050] To obtain the global optimal solution and accelerate the solution speed, a damping factor λ and a relaxation factor ω are introduced k :

[0051] where ω k is selected such that: ||X k+1 || < ||X k || holds;

[0052] To reduce the number of equation solutions and improve efficiency, a correction method is used for solution:

[0053]

[0054] Preferably, the above step 5 includes:

[0055] Step 51: Numerically simulate the response simulation values corresponding to the model parameters. The response simulation values have the same physical meaning as the input curve;

[0056] Step 52: Compare the measured curve with the response simulation value curve, set the reliability evaluation conditions, and evaluate the reliability of the inversion parameters according to the curve shape and the magnitude of the curve response value;

[0057] Step 53: For the inversion results judged to be reliable, output the results in the agreed format; for the inversion results judged to be unreliable, optimize the results through manual intervention and then output the results in the agreed format.

[0058] The present invention utilizes the resistivity and direction signals of the azimuth boundary detection tool while drilling to perform real-time calculation of the boundary distance, with a fast calculation speed and high calculation efficiency. It uses the method of constructing an analytical function to correlate well logging responses with model parameters, and obtains formation dip angle, boundary distance, resistivity of surrounding rock, and resistivity of target layer by solving the non-linear equations with numerical methods. The analytical method has higher calculation accuracy, and the calculation results do not depend on the initial model, avoiding a large impact of human factors on the calculation results. It is a technology that is more suitable for wide-range promotion and application. In addition, the calculation results of the present invention include dip angle, boundary distance, resistivity of surrounding rock, and resistivity of the current layer, which have the key parameters required in the geological steering process. Combining with wellbore trajectory data, it can clarify the wellbore-formation position relationship and bit adjustment strategy, and quantitatively guide the bit adjustment by quantifying the distance from the measurement point to the boundary, fully meeting the needs of geological steering applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] FIG. Figure 1 shows the flow chart of real-time quantitative calculation of the geological steering boundary distance of the horizontal well in the embodiment of the present invention.

[0060] FIG. Figure 2 shows the schematic diagram of the horizontal well formation model in the embodiment of the present invention.

[0061] FIG. Figure 3 shows an example of the relationship between resistivity well logging response and boundary distance variation (investigation of geological signals of AziTrak675 electromagnetic wave instrument while drilling).

[0062] FIG. Figure 4 shows an example of the relationship between direction signal strength and boundary distance variation.

[0063] FIG. Figure 5 shows an example of the relationship between the high side azimuth of the direction signal and the formation interface azimuth variation.

[0064] FIG. Figure 6 shows an example of the horizontal well steering result processed by the method described in the embodiment of the present invention.

[0065] FIG. Figure 2 In which, a is the upper surrounding rock, b is the target layer, c is the lower surrounding rock, d is the upper boundary, e is the lower boundary, f is the wellbore, and g is the boundary distance. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0066] The present invention is not limited by the following embodiments, and the specific implementation manners can be determined according to the technical solutions of the present invention and the actual situation.

[0067] The present invention will be further described below in conjunction with embodiments:

[0068] Embodiment 1: As shown in FIG. Figure 1 The method for real-time quantitative calculation of the boundary distance for horizontal well geological steering includes the following steps:

[0069] Step 1 includes:

[0070] Establish a formation model library including a horizontal well formation model and a highly deviated well formation model;

[0071] Numerical simulation calculation converts the formation model parameter library into a logging response library of the logging-while-drilling tool.

[0072] In Step 1, the establishment of the formation model includes the following:

[0073] 1) Select a logging-while-drilling electromagnetic resistivity tool, and obtain its detection range and resistivity range according to the instrument performance;

[0074] 2) Establish a single-layer model library, such as Figure 2 , the instrument is placed in the middle between the upper and lower interfaces. According to the resistivity range of the instrument, the resistivity value of each layer changes from small to large. According to the farthest investigation ability and resolution of the instrument, the formation thickness is set as the maximum and minimum values, and the relative dip angle between the wellbore and the formation changes from 0 degree to 90 degrees;

[0075] 3) Arrange the formation model parameters in ascending order of values according to the order of dip angle, upper formation resistivity, target layer resistivity, lower formation resistivity, and boundary distance to form a formation model library;

[0076] 4) Use the numerical simulation method to perform simulation calculations one by one for the logging response of the azimuth investigation tool under the model conditions, and save the resistivity logging response, geological signal response intensity, and geological signal high-side azimuth value corresponding to the logging response in the simulation order;

[0077] 5) Save the simulated signal response in the corresponding model order to form a logging response library of the logging-while-drilling tool.

[0078] So far, Step 1 is completed, and the process enters the next step.

[0079] Step 2 includes:

[0080] According to the relationship between the logging response value of the instrument in the response library and the dip angle, boundary distance, formation resistivity, and formation resistivity; construct a functional relationship between the model parameters and the logging apparent value;

[0081] In Step 2, the investigation of the relationship between the model parameters and the response value and the construction of the functional relationship include the following:

[0082] 1) Fix three of the four types of parameters of dip angle, boundary distance, formation resistivity, and formation resistivity, and vary one of them to investigate the relationship between the model parameters and the resistivity curves at different detection depths, such as Figure 3 ;

[0083] 2) Fix three of the four parameters, namely, inclination angle, boundary distance, surrounding rock resistivity, and formation resistivity, and change one of them to investigate the relationship between the model parameters and the directional signal intensity curve, such as Figure 4 ;

[0084] 3) Fix three of the four parameters, namely, inclination angle, boundary distance, surrounding rock resistivity, and formation resistivity, and change one of them to investigate the relationship between the model parameters and the high side orientation of the directional signal, such as Figure 5 ;

[0085] 4) According to the relationship between the model parameters and the simulation curve values, the functional relationship between the model parameters and each logging response is determined through semi-quantitative analysis;

[0086] 5) After determining the functional relationship, the curve fitting method is used to calculate the functional relationship between the model parameters and the response as the integral operator of the functional;

[0087] 6) By combining multiple logging curve responses with integral operators, a corresponding relationship between the model parameter library and the logging response library is formed, and the corresponding relationship is expressed as a nonlinear equation group through the integral operator.

[0088] At this point, step 2 is completed and the process proceeds to the next step:

[0089] Step 3 includes:

[0090] The well site acquires logging data in real time, including four apparent resistivity curves, directional signal strength curve, and directional signal high side position curve, and establishes a formation parameter-measurement response nonlinear equation group based on the functional mapping relationship;

[0091] In step 3, according to the measured data and the functional relationship constructed in step 2, the formation parameter-logging response equation group is established, including:

[0092] 1) Select four resistivity curves, one directional signal strength curve, and one directional signal high side azimuth curve, a total of six curves;

[0093] 2) Substitute the six curve response values into the functional mapping relationship determined by the functional established in step 2 to form an equation relationship from the model parameters to the logging response set;

[0094] 3) Move the terms on the right side of the six equations to the left side, and combine the equations to form six nonlinear equations.

[0095] At this point, step 3 is completed and the process proceeds to the next step:

[0096] Step 4 includes:

[0097] Solve nonlinear equations, calculate inclination, boundary distance, formation resistivity, and surrounding rock resistivity parameters, and obtain the distance information between the wellbore and the formation boundary in real time;

[0098] In step 4, solve the system of equations to obtain the distance information from the wellbore to the layer boundary in real time, including:

[0099] 1) The system of equations is the analytical relationship between the formation model parameters and the logging response;

[0100] 2) The system of equations is a non-linear system of equations, and each model parameter has a coupled influence on the logging response;

[0101] 3) The mapping relationship of the system of equations is based on the model logging response, and the solution exists. When it is inconvenient to use the analytical method to solve, the numerical method can be used for solution.

[0102] So far, step 4 is completed, and the process enters the next step:

[0103] Step 5 includes:

[0104] Numerically simulate and calculate the response values corresponding to the model parameters, match them with the measured curve, obtain the quality control curve, and output the calculation results.

[0105] In step 5, simulate and calculate the solution results and output the calculation results, including:

[0106] 1) Set the formation model according to the calculated model parameters, simulate and calculate its logging response, compare it with the measured well data, and analyze the accuracy of the calculation results;

[0107] 2) When the accuracy requirement is not met, manual intervention is required to add constraints to make the calculation results reasonable;

[0108] 3) Format and output the calculation results that meet the accuracy requirements, such as Figure 6 .

[0109] So far, the entire process is completed.

[0110] This embodiment includes the establishment of a numerical simulation library, the construction of the mapping relationship between the model parameter library (formation model library) and the instrument response library, the establishment and solution of the system of equations for solving formation parameters, the output and constraint of the inversion results, etc. This method can analyze the formation parameters based on the logging response without the initial model condition, eliminate the problem that the results fall into the local optimal solution, and at the same time make the information relied on for solving the formation parameters less and the guiding application more flexible.

[0111] Embodiment 2: The method for real-time quantitative calculation of the boundary distance for horizontal well geosteering includes the following steps:

[0112] Step 1: Establish a formation model library including a horizontal well formation model and a highly deviated well formation model; numerically simulate and calculate to convert the model parameter library into a logging response library of the logging-while-drilling instrument.

[0113] Among them:

[0114] Step 11: Select the western drilling azimuth boundary detection tool MAPR. The instrument has a resistivity range of 0.2 ohmm to 2000.0 ohmm, a boundary detection distance of 0.0 m to 5.3 m, a resolution of 0.2 m, a formation azimuth recognition ability of 0 degrees to 360 degrees, and an applicable well deviation change range of 0.0 degrees to 180 degrees; establish a formation model library according to the instrument range, boundary detection distance, and applicable range;

[0115] Step 12: Set the parameters of the formation model library. The model library is a single-layer horizontal formation model. The relative dip angle between the wellbore and the formation is changed by modifying the well deviation angle. The model parameters include:

[0116] Well deviation angle: 0 degrees to 90 degrees;

[0117] Layer thickness: 0.2 m to 10.6 m;

[0118] Distance from the upper boundary: 0 m to 5.3 m;

[0119] Resistivity of the upper surrounding rock: 0.2 ohmm to 2000.0 ohmm

[0120] Resistivity of the middle layer (target layer resistivity): 0.2 ohmm to 2000.0 ohmm

[0121] Resistivity of the lower surrounding rock: 0.2 ohmm to 2000.0 ohmm

[0122] Step 13: Group the model parameters according to the combination of dip angle - layer thickness - distance from the upper boundary - resistivity of the upper surrounding rock - resistivity of the middle layer - resistivity of the lower surrounding rock. In each group, each variable takes 25 equally spaced values from small to large within the corresponding value range, and the values cover the upper and lower boundaries. Each value combination forms a model parameter sampling point;

[0123] Step 14: Sort the model parameter sampling points in ascending order according to the priority levels of dip angle, layer thickness, boundary distance, resistivity of the upper surrounding rock, resistivity of the middle layer, and resistivity of the lower surrounding rock to form a model library;

[0124] Step 15: Use the finite element method to establish a numerical simulation program for the western drilling azimuth boundary detection tool MAPR. Read the model parameters in the model library in sequence, calculate the corresponding simulated responses and arrange them in the calculation order to form a simulated response library.

[0125] Step 2: According to the relationship between the logging response values of the instrument in the simulated response library and the dip angle, boundary distance, formation resistivity, and surrounding rock resistivity; construct the functional relationship between the model parameters and the logging apparent values;

[0126] Among them:

[0127] Step 21: Fix three of the four parameters of dip angle, boundary distance, resistivity of surrounding rock, and resistivity of formation, and vary one of them to examine the relationship between model parameters and resistivity curves at different detection depths. Taking the change of dip angle as an example, the dip angle ranges from 0° to 90°, the upper boundary distance is 0.2 m, the layer thickness is 6 m, the resistivity of the upper surrounding rock is 2 ohm·m, the resistivity of the middle layer is 20 ohm·m, and the resistivity of the lower surrounding rock is 3 ohm·m. Examine the relationship between the logging response and the change of dip angle;

[0128] Step 22: Fix three of the four parameters of dip angle, boundary distance, resistivity of surrounding rock, and resistivity of formation, and vary one of them to examine the relationship between model parameters and direction signal strength curves. Taking the change of boundary distance as an example, the boundary distance ranges from 0 m to 5.3 m, the dip angle is 90°, the resistivity of the upper surrounding rock is 2 ohm·m, the resistivity of the middle layer is 20 ohm·m, the resistivity of the lower surrounding rock is 2 ohm·m, and the layer thickness is 8 m. Examine the relationship between the logging response and the change of boundary distance;

[0129] Step 23: Fix three of the four parameters of dip angle, boundary distance, resistivity of surrounding rock, and resistivity of formation, and vary one of them to examine the relationship between model parameters and the high-side azimuth of the direction signal. Taking the change of dip angle as an example, the dip angle ranges from 0° to 90°, the upper boundary distance is 0.2 m, the layer thickness is 6 m, the resistivity of the upper surrounding rock is 2 ohm·m, the resistivity of the middle layer is 20 ohm·m, and the resistivity of the lower surrounding rock is 3 ohm·m. Examine the relationship between model parameters and the high-side azimuth of the direction signal;

[0130] Step 24: According to the relationship between model parameters and simulation values, the closer to the boundary, the greater the curve separation value. The curve response value is inversely proportional to the boundary distance, directly proportional to the resistivity contrast on both sides of the interface, inversely proportional to the conductivity difference on both sides of the interface, and directly proportional to the dip angle. Establish the relationship between model parameters and logging response, where:

[0131] The resistivity curve satisfies the following functional relationship:

[0132]

[0133] where Ra is the apparent resistivity, DTB is the distance to the nearest boundary, Rs is the resistivity of the surrounding rock of the nearest boundary, Rt is the resistivity of the target layer, Dip is the relative dip angle between the wellbore and the formation, and f is the undetermined mapping relationship.

[0134] The direction signal curve satisfies the following functional relationship:

[0135]

[0136] where, signal str , sigal amzThey are the geological signal intensity and the high-side azimuth of the geological signal respectively. DTB is the distance to the nearest boundary, Rs is the resistivity of the surrounding rock of the nearest boundary, Rt is the resistivity of the target layer, Dip is the relative dip angle between the wellbore and the formation, and g and h are the undetermined mapping relationships.

[0137] Step 25: After determining the expression of formula (1), fix the boundary distance and relative dip angle, change the resistivity of the surrounding rock and the resistivity of the target layer, sort the resistivity of the surrounding rock and the resistivity of the target layer that satisfy P(Ra)=Ra in formula (1) from small to large, and fit and regress the two-dimensional surface equation with the resistivity of the target layer and the resistivity of the surrounding rock as independent variables and Ra as the dependent variable, and record the equation expression as f(Ra);

[0138] Step 26: After determining the expression of formula (2), fix the resistivity of the surrounding rock and the resistivity of the target layer, change the relative dip angle and the boundary distance, and sort the boundary distance and relative dip angle that satisfy s(signal str , sigal amz )=(signal str , sigal amz ) from small to large to form a two-dimensional array. Fit and regress the equation with the boundary distance and relative dip angle as independent variables and (signal str , sigal amz ) as the dependent variable, and record the equation expression as g(DTB)-h(Dip);

[0139] Step 27: Sort the equation coefficients from small to large in the priority order of apparent resistivity, direction signal intensity, and direction signal azimuth, and establish an equation coefficient library;

[0140] Step 28: According to the equation coefficients and the response values, the equations corresponding to the coefficient library satisfy:

[0141]

[0142] In the formula, Ra1, Ra2, Ra3, and Ra4 represent the apparent resistivity curves of four different detection depths.

[0143] Step 3 includes:

[0144] Obtain well logging data in real time at the well site, including four apparent resistivity curves, a direction signal intensity curve, and a curve of the azimuth where the direction signal high side is located, and establish a non-linear equation system of formation parameters - measurement response according to the functional mapping relationship;

[0145] Among them:

[0146] Step 31: Select four resistivity curves and upload them in real time. The resistivity curves are denoted as Ra1, Ra2, Ra3, and Ra4, and a direction signal intensity curve signal str、A direction signal high side azimuth curve Signal amz ;

[0147] Step 32: Establish the functional relationship between the response and the model according to formula (3):

[0148]

[0149]

[0150]

[0151]

[0152]

[0153]

[0154] Step 33: Move the terms on the right side of the six equations to the left side of the equations, and combine the equations to form six nonlinear equations:

[0155]

[0156] Step 4 includes: solving a nonlinear equation group, calculating the inclination angle, boundary distance, formation resistivity, and surrounding rock resistivity parameters, and obtaining the distance information between the wellbore and the formation boundary in real time;

[0157] in:

[0158] Step 41: For ease of writing, the six equations in formula (10) (four resistivity + 1 geological signal + 1 azimuth indication combination) can be transformed into a problem of solving six unknown quantities. That is, the equation group to be solved is:

[0159] H i (x1, x2, x3, x4, x5, x6) = 0i = 1, 2, ..., 6 (11)

[0160] That is, the unknown quantity to be solved is:

[0161] X=(x1,x2,x3,x4,x5,x6) T

[0162] H i is the i-th of the six equations, where x1, x2, x3, x4, x5, and x6 are unknown quantities to be solved, representing the dip angle, boundary distance (upper and lower), resistivity of the target layer, and resistivity of the surrounding rock (upper and lower), respectively.

[0163] Where:

[0164]

[0165] To obtain the global optimal solution and accelerate the solution speed, a damping factor λ and a relaxation factor ω are introduced k :

[0166] where ω k is selected such that: ||X k+1 || < ||X k || holds.

[0167] To reduce the number of equation solutions and improve efficiency, the correction method can be used for solution:

[0168]

[0169] Step 5 includes: numerically simulating the response values corresponding to the model parameters, matching them with the measured curve, obtaining the quality control curve, and outputting the calculation results.

[0170] Among them:

[0171] Step 51: Set the formation model according to the calculated model parameters, simulate and calculate its logging response, compare and analyze the accuracy of the calculation results with the measured well data, and the relative error between the simulation result and the measured result less than 5% is an acceptable result;

[0172] When the accuracy requirement is not met, manual intervention is required. By manually correcting the dip angle and boundary distance as additional constraints, calculate within the constraint range to make the minimum relative error between the simulation result and the measured response as the final calculation result;

[0173] Step 53: Format and output the calculation results that meet the accuracy requirements.

[0174] So far, the whole process is completed.

[0175] The above technical features constitute the embodiments of the present invention, which have strong adaptability and implementation effects. Non-essential technical features can be added or reduced according to actual needs to meet the requirements of different situations.

Claims

1. A method for real-time quantification of the boundary distance in horizontal well geosteering calculation, characterized in that It includes the following steps: Step 1: Establish a horizontal well formation model and a highly deviated well formation model to form a formation model library. The model parameters in the formation model library include four types of parameters: dip angle, boundary distance, resistivity of surrounding rock, and formation resistivity. Numerical simulation calculation converts the formation model library into a logging response library of the logging-while-drilling instrument. The response library includes resistivity curves with four different detection depths, a direction signal intensity curve, and an azimuth curve of the high side of the direction signal; Step 2: Construct a functional relationship between the model parameters and the logging apparent value according to the relationship between the logging response value of the instrument in the response library and the dip angle, boundary distance, formation resistivity, and resistivity of surrounding rock; Step 3: Obtain logging data in real time at the well site, including four apparent resistivity curves, a direction signal intensity curve, and an azimuth curve of the high side of the direction signal. Establish a non-linear equation set of formation parameters - measurement response according to the functional mapping relationship; Step 4: Solve the non-linear equation set, calculate the parameters of dip angle, boundary distance, formation resistivity, and resistivity of surrounding rock, and obtain the distance information of the wellbore from the formation boundary in real time; Step 5: Numerically simulate and calculate the response values corresponding to the model parameters, match them with the measured curves, obtain the quality control curve, and output the inversion result; Step 2 includes: Step 21: Examine the corresponding relationship between the dip angle, boundary distance, formation resistivity, resistivity of surrounding rock and the logging response, analyze the variation law they satisfy, and determine the functional relationship between the dip angle, boundary distance, formation resistivity, resistivity of surrounding rock and the logging response through semi-quantitative analysis; Step 22: Use curve fitting to extract the characteristic curves, combine the functional relationship between the model parameters and the simulated response, determine the mapping relationship between the simulated response and the formation parameters, and complete the construction of the functional relationship between the model parameters and the simulated response; In Step 2, the resistivity curve satisfies the following functional relationship: （1） Among them R a is the apparent resistivity, DTB is the distance to the nearest boundary, R s is the resistivity of the surrounding rock of the nearest boundary, R t is the resistivity of the target layer, Dip is the relative dip angle between the wellbore and the formation, f is the mapping relationship to be determined; The direction signal curve satisfies the following functional relationship: （2） Among them, signal str , sigal amz are the geological signal intensity and the high side azimuth of the geological signal respectively, DTB is the distance to the nearest boundary, R s is the resistivity of the surrounding rock of the nearest boundary, R t is the resistivity of the target layer, Dip is the relative dip angle between the wellbore and the formation, and g, h are undetermined mapping relationships; After determining the expression of formula (2), fix the resistivity of the surrounding rock and the resistivity of the target layer, change the relative dip angle and the boundary distance, and use s(signal str ,sigal amz )=(signal str ,sigal amz ) to form a two-dimensional array by sorting the boundary distances and relative dip angles from small to large. Using the boundary distance and relative dip angle as independent variables and (signal str ,sigal amz ) as the dependent variable, fit the regression equation and record the equation expression as ; Sort the equation coefficients in ascending order according to the priority order of apparent resistivity, direction signal intensity, and direction signal azimuth to establish an equation coefficient library; According to the equation coefficients and the response values, the corresponding equations in the coefficient library satisfy: （3） In the formula, represents four apparent resistivity curves with different detection depths.

2. The method for real-time quantification of the boundary distance for horizontal well geosteering calculation according to claim 1, characterized in that Step 1 includes: Step 11: Establish a horizontal well formation model and a highly deviated well formation model. The model parameters include dip angle, boundary distance, resistivity of surrounding rock, and formation resistivity; Step 12: Numerical simulation calculation converts the model parameter library into a logging response library of the logging-while-drilling instrument. The simulation tool is for a multi-depth electromagnetic wave resistivity instrument, with resistivity curves of four different measurement depths, providing a direction signal intensity curve and an azimuth signal curve of the high side of the direction signal.

3. The method for real-time quantification of the boundary distance for horizontal well geological steering calculation according to claim 1 or 2, characterized in that Step 3 includes: Step 31: Obtain logging data in real time at the well site, including four apparent resistivity curves with different detection depths, a direction signal intensity curve, and an azimuth curve of the high side of the direction signal; Step 32: Substitute the measured values into the functional mapping relation satisfied by the corresponding curves in turn, establish an equation of the measurement response and the function association, and the equation relations established by multiple resistivity curves and direction signals form a non-linear equation set of formation parameters - measurement response; Select four resistivity curves for real-time upload. The resistivity curves are denoted as , one direction signal intensity curve , one high side azimuth curve of the direction signal , Establish a functional relation between the response and the model according to formula (3): （4） （5） （6） （7） （8） （9） Step 33: Move the terms on the right side of equations (4) to (9) to the left side of the equations and combine the equations to form a system of six nonlinear equations: （10）。 4. The method for real-time quantification of the boundary distance for horizontal well geological steering calculation according to claim 3, characterized in that In step 4, the six equations of formula (10) are transformed into a problem of solving six unknown quantities, that is, the set of equations to be solved is: （11） The unknown quantity to be solved is: is the i-th of six equations, where are the unknowns to be solved, representing dip angle, upper and lower boundary distances, resistivity of target layer, resistivity of upper and lower surrounding rocks respectively; Where: To obtain the global optimal solution and accelerate the solution speed, a damping factor and a relaxation factor are introduced: Among them should be selected such that: holds; In order to reduce the number of equation solutions and improve efficiency, the correction method is used to solve the equation: 。 5. The method for real-time quantification of the boundary distance for horizontal well geological steering calculation according to claim 1 or 2, characterized in that Step 5 includes: Step 51: numerical simulation calculates the response simulation value corresponding to the model parameter, and the response simulation value has the same physical meaning as the input curve; Step 52: Compare the measured curve with the response simulation value curve, set reliability evaluation conditions, and evaluate the reliability of the inversion parameters according to the curve shape and the curve response value; Step 53: If the inversion result is judged to be reliable, the result is output; if the inversion result is judged to be unreliable, the result is optimized through manual intervention and then the result is output.

6. The method for real-time quantification of the boundary distance for horizontal well geosteering calculation according to claim 3, wherein Step 5 includes: Step 51: numerical simulation calculates the response simulation value corresponding to the model parameter, and the response simulation value has the same physical meaning as the input curve; Step 52: Compare the measured curve with the response simulation value curve, set reliability evaluation conditions, and evaluate the reliability of the inversion parameters according to the curve shape and the curve response value; Step 53: If the inversion result is judged to be reliable, the result is output; if the inversion result is judged to be unreliable, the result is optimized through manual intervention and then the result is output.

7. The method for real-time quantification of the boundary distance for horizontal well geological steering calculation according to claim 4, wherein Step 5 includes: Step 51: numerical simulation calculates the response simulation value corresponding to the model parameter, and the response simulation value has the same physical meaning as the input curve; Step 52: Compare the measured curve with the response simulation value curve, set reliability evaluation conditions, and evaluate the reliability of the inversion parameters according to the curve shape and the curve response value; Step 53: If the inversion result is judged to be reliable, the result is output; if the inversion result is judged to be unreliable, the result is optimized through manual intervention and then the result is output.

8. The method for real-time quantitative calculation of the boundary distance for horizontal well geosteering according to claim 5, characterized in that Step 5 includes: Step 51: numerical simulation calculates the response simulation value corresponding to the model parameter, and the response simulation value has the same physical meaning as the input curve; Step 52: Compare the measured curve with the response simulation value curve, set reliability evaluation conditions, and evaluate the reliability of the inversion parameters according to the curve shape and the curve response value; Step 53: If the inversion result is judged to be reliable, the result is output; if the inversion result is judged to be unreliable, the result is optimized through manual intervention and then the result is output.

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