Horizontal well guiding abnormity early warning method and device, storage medium and equipment

Abstract

The invention discloses a horizontal well guiding abnormity early warning method and device, a storage medium and equipment, and belongs to the technical field of well drilling and completion of petroleum and natural gas engineering. Comprising the following steps: acquiring three-dimensional position coordinate data, GR data while drilling, drilling time data and lithologic logging data of a drilled well in a whole target block as an input data set; performing simulation according to the GR data while drilling of the drilled well to obtain GR data while drilling of a new drilled well, and combining the input data set to obtain the predicted depth of a drill bit of the new drilled well through the established horizontal section intelligent guiding judgment model; and according to the predicted depth and the target stratum, whether abnormity occurs in the drilling process or not is judged. According to the method, the horizontal section well track prediction model in the drilling process is constructed based on the artificial intelligence method, the problems that in the horizontal well drilling process, the drilling horizon cannot be found in time, and a drill bit cannot be automatically adjusted are solved, the horizontal well drilling efficiency and the reservoir drilling rate are improved, the oil and gas reservoir single well yield and recovery efficiency are improved, and the site construction cost is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of oil and gas engineering drilling and completion, and in particular to a horizontal well steering abnormality early warning method, device, storage medium and equipment. Background Art

[0002] In the field of oil and gas drilling and production, with the acceleration of the pace of unconventional oil and gas exploration, horizontal well drilling has gradually become the mainstream. The horizontal section of the horizontal well is prone to penetration problems during drilling, so it is necessary to use geosteering technology to predict the horizontal well. Traditional geosteering technology collects underground drilling and geological parameter information through measurement while drilling tools, analyzes the contact relationship between the drilling trajectory and the formation, and grasps the wellbore trajectory and the spatial distribution of the reservoir. The geosteering software in the ground system processes and analyzes the received information and guides the next drilling, so as to achieve accurate landing of the horizontal well and improve the reservoir drilling rate.

[0003] However, traditional geo-steering technology requires manual judgment and issuance of drilling instructions, resulting in low drilling efficiency and reservoir drilling rate, making it difficult to improve the single well production and recovery rate of complex oil and gas reservoirs. Summary of the invention

[0004] In order to solve the defects of the prior art, the present invention provides a horizontal well guidance abnormality warning method, device, storage medium and equipment, which solves the problems of being unable to timely detect the drilled layer and automatically adjust the drill bit during the horizontal well drilling process, improves the coalbed methane horizontal well drilling efficiency and reservoir drilling rate, increases the single well production and recovery rate of complex oil and gas reservoirs, and reduces on-site construction costs.

[0005] The present invention provides the following technical solutions:

[0006] In a first aspect, the present invention provides a horizontal well guidance abnormality early warning method, the method comprising:

[0007] Obtain the three-dimensional position coordinate data, GR data while drilling, drilling time data and lithology logging data of the drilled wells in the entire target block as input data sets;

[0008] Simulating the GR while drilling data of a newly drilled well in the target block based on the GR while drilling data of several wells that have been drilled in the target block, and obtaining the predicted depth of the drill bit of the newly drilled well based on the simulated GR while drilling data of the newly drilled well and the input data set through the established horizontal section intelligent guidance judgment model;

[0009] Based on the predicted depth of the drill bit of the new well and the target formation, it is determined whether an abnormality occurs during the drilling process of the new well.

[0010] Furthermore, the input data set is obtained by the following method:

[0011] Based on the original data collected on-site in the target block, the GR data, drilling time data and lithology logging data of the wells drilled in the target block are sorted out;

[0012] Load the sorted GR data, drilling time data and lithology logging data into the 3D geological modeling software to establish a 3D geological model of the target block;

[0013] The three-dimensional coordinate data of the geographical location of the drilled wells in the entire target block, the GR data while drilling, the drilling time data, the lithology logging data, the established three-dimensional geological model and the three-dimensional coordinate data of the geographical location of the drilled wells in the three-dimensional geological model are exported according to the set accuracy to obtain the input data set.

[0014] Furthermore, the simulating of obtaining the GR while drilling data of a new well in the target block based on the GR while drilling data of several wells that have been drilled in the target block includes:

[0015] Several wells drilled nearest to the new well in the target block are used as reference wells;

[0016] The GR data of a part of the well section of the reference well while drilling is used as the GR value of the corresponding well section of the new well, and the GR data of the new well while drilling is simulated.

[0017] Furthermore, the predicted depth of the drill bit of the new well is obtained by using the GR data while drilling of the new well obtained by simulation and the input data set through the established horizontal section intelligent guidance judgment model, including:

[0018] According to the GR data of the new well obtained by simulation, the horizontal section intelligent steering judgment model is established;

[0019] The simulated GR data of the newly drilled well and the input data set are used as inputs of the horizontal section intelligent guidance judgment model to output the probability distribution of the specific depth of the drill bit of the newly drilled well;

[0020] The maximum value in the probability distribution is taken as the predicted depth of the drill bit for the new well.

[0021] Furthermore, the probability distribution of the specific depth of the drill bit of the newly drilled well is calculated by the following formula:

[0022]

[0023] in, represents the probability distribution of a specific depth φ, i represents the i-th measurement point in the well, γ represents the GR data while drilling, j represents the j-th point in the depth domain corresponding to the i-th measurement point, f ( ) represents the probability density function, and p represents the prior distribution probability.

[0024] Further, judging whether an abnormality occurs during the drilling of the new well according to the predicted depth of the drill bit of the new well and the target formation obtained includes:

[0025] Determine whether the predicted depth of the drill bit of the new well has penetrated the top interface and the bottom interface of the target formation. If so, the drill bit has penetrated the formation, and an abnormality is determined and an alarm is issued;

[0026] The data corresponding to the abnormal point is included in the original data, and the original data is updated.

[0027] Furthermore, the method further comprises:

[0028] The three-dimensional geological model is updated in real time through updated original data.

[0029] In a second aspect, the present invention provides a horizontal well guidance abnormality early warning device, the device comprising:

[0030] The data acquisition module is used to obtain the three-dimensional position coordinate data, GR data while drilling, drilling time data and lithology logging data of the drilled wells in the entire target block as input data sets;

[0031] A prediction module is used to simulate the GR while drilling data of a new well in the target block based on the GR while drilling data of several wells that have been drilled in the target block, and obtain the predicted depth of the drill bit of the new well based on the simulated GR while drilling data of the new well and the input data set through the established horizontal section intelligent guidance judgment model;

[0032] The early warning module is used to determine whether an abnormality occurs during the drilling process of the new well according to the predicted depth of the drill bit of the new well and the target formation.

[0033] Furthermore, the input data set is obtained through the following process:

[0034] The original data acquisition unit is used to obtain the GR data, drilling time data and lithology logging data of the wells drilled in the target block according to the original data collected on site in the target block;

[0035] The 3D geological model building unit is used to load the sorted GR data, drilling time data and lithology logging data into the 3D geological modeling software to build a 3D geological model of the target block;

[0036] The input data set export unit is used to export the three-dimensional coordinate data of the geographical location of the drilled wells in the entire target block, the GR data while drilling, the drilling time data, the lithology logging data, the established three-dimensional geological model and the three-dimensional coordinate data of the geographical location of the drilled wells in the three-dimensional geological model according to the set accuracy to obtain the input data set.

[0037] Furthermore, the prediction module includes:

[0038] A reference well prediction unit is used to use several wells that have been drilled closest to the newly drilled well in the target block as reference wells;

[0039] The GR simulation unit is used to use the while-drilling GR data of a part of the well section of the reference well as the GR value of the corresponding well section of the new well, so as to simulate and obtain the while-drilling GR data of the new well.

[0040] Furthermore, the prediction module also includes:

[0041] A model building unit is used to build a horizontal section intelligent steering judgment model based on the GR data of the new well obtained by simulation;

[0042] A probability distribution prediction unit, used to use the simulated GR data of the new well and the input data set as inputs to the horizontal section intelligent guidance judgment model, and output the probability distribution of the specific depth of the drill bit of the new well;

[0043] The predicted depth determination unit is used to take the maximum value in the probability distribution as the predicted depth of the drill bit for the new drilling.

[0044] Furthermore, the probability distribution of the specific depth of the drill bit of the newly drilled well is calculated by the following formula:

[0045]

[0046] in, represents the probability distribution of a specific depth φ, i represents the i-th measurement point in the well, γ represents the GR data while drilling, j represents the j-th point in the depth domain corresponding to the i-th measurement point, f ( ) represents the probability density function, and p represents the prior distribution probability.

[0047] Furthermore, the early warning module includes:

[0048] An early warning unit is used to determine whether the predicted depth of the drill bit of the new well has penetrated the top interface and the bottom interface of the target formation. If so, the drill bit has penetrated the layer, and an abnormality is determined and an alarm is issued;

[0049] The data updating unit is used to incorporate the data corresponding to the point where the abnormality occurs into the original data and update the original data.

[0050] Furthermore, the device also includes:

[0051] The model updating module is used to update the three-dimensional geological model in real time through updated original data.

[0052] In a third aspect, the present invention provides a computer-readable storage medium for horizontal well steering abnormality warning, comprising a memory for storing processor executable instructions, which, when executed by the processor, implements the steps of the horizontal well steering abnormality warning method described in the first aspect.

[0053] In a fourth aspect, the present invention provides a device for horizontal well steering abnormality warning, characterized in that it includes at least one processor and a memory storing computer executable instructions, and when the processor executes the instructions, it implements the steps of the horizontal well steering abnormality warning method described in the first aspect.

[0054] The present invention has the following beneficial effects:

[0055] The present invention constructs a horizontal well trajectory prediction model during drilling based on artificial intelligence methods, overcomes the drill bit positioning problem during CBM horizontal well drilling, and solves the problems of not being able to find the drilled-out layer in time and automatically adjust the drill bit during horizontal well drilling. It greatly improves the CBM horizontal well drilling efficiency and reservoir drilling rate, significantly improves the single well production and recovery rate of complex oil and gas reservoirs, and reduces on-site construction costs. In addition, it reduces the energy and resource waste caused by correction drilling or shutdown due to the drill bit drilling out of the layer, making the drilling and mining of oil and natural gas more green, energy-saving, low-carbon, environmentally friendly and efficient. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 It is a flow chart of the horizontal well steering abnormality early warning method of the present invention;

[0057] Figure 2 Results are presented for the final prediction using a single well as the reference well;

[0058] Figure 3 Results are presented for the final prediction using two wells as reference wells;

[0059] Figure 4 It is a schematic diagram of the horizontal well steering abnormality early warning device of the present invention. DETAILED DESCRIPTION

[0060] In order to make the technical problems, technical solutions and advantages to be solved by the present invention clearer, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings and specific embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. The components of the embodiments of the present invention generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making creative work belong to the scope of protection of the present invention.

[0061] The embodiment of the present invention provides a horizontal well guidance abnormality early warning method, such as Figure 1 As shown, the method includes:

[0062] S1: Obtain the three-dimensional position coordinate data, while-drilling GR (natural gamma ray) data, drilling time data and lithology logging data of the drilled wells in the entire target block as input data sets.

[0063] As an example, the specific implementation process of this step includes:

[0064] S11: Based on the original data collected on-site in the target block, the GR data while drilling, drilling time data and lithology logging data of the wells drilled in the target block are sorted out.

[0065] For example, based on the original data collected on-site in a target block in the Ordos Basin, data cleaning can be performed according to the law of increasing gas well depth (including checking data anomalies and supplementing data missing values, etc.), and the GR data, drilling time data and lithology logging data of the target block can be sorted out. As an improvement, optionally, gas logging data and corresponding well depth information can also be sorted out at the same time, which can also be included in the subsequent input data set.

[0066] S12: Load the sorted GR data while drilling, drilling time data and lithology logging data into the 3D geological modeling software to establish a 3D geological model of the target block.

[0067] For example, the cleaned data can be organized into a standard format and loaded into the PETREL software to build a three-dimensional geological model;

[0068] S13: The three-dimensional coordinate data of the geographical location of the drilled wells in the entire target block (i.e., the X, Y, Z data of the geographical location coordinates), GR data while drilling, drilling time data, lithologic logging data, the established three-dimensional geological model, and the three-dimensional coordinate data of the three-dimensional coordinate data of the geographical location of the drilled wells in the three-dimensional geological model (i.e., the X, Y, Z data of the geographical location coordinates corresponding to the X, Y, Z data in the three-dimensional geological model) are exported according to the set accuracy to obtain an input data set.

[0069] For example, the above data can be exported with an accuracy of 0.125m for a single well depth, and the data of all wells can be merged into the same data table as the input data set.

[0070] S2: The GR while drilling data of a new well in the target block is simulated based on the GR while drilling data of several wells that have been drilled in the target block. Based on the simulated GR while drilling data of the new well and the input data set, the predicted depth of the drill bit of the new well is obtained by establishing a horizontal section intelligent guidance judgment model.

[0071] As an improvement of the embodiment of the present invention, an implementation of step S2 includes:

[0072] S21: Use several (eg, one or more) wells nearest to the newly drilled well in the target block as reference wells.

[0073] S22: Using the while-drilling GR data of a part of the well section of the reference well as the GR value of the corresponding well section of the new well, so as to simulate the while-drilling GR value obtained during the actual drilling of the new well, and finally simulating the while-drilling GR data of the new well, that is, the GR curve.

[0074] In this step, the GR data while drilling of a well that has been drilled can be used to simulate the GR data while drilling of a new well, or the GR data while drilling of different sections of multiple wells that have been drilled can be used to simulate the GR data while drilling of a new well.

[0075] S23: Establish a horizontal section intelligent steering judgment model based on the GR data of the new well obtained by simulation.

[0076] For example, based on the Bayesian inversion algorithm and the GR curve obtained by simulation, an intelligent horizontal section guidance judgment model can be established, and the problem of drill bit positioning during coalbed methane horizontal well drilling can be overcome through artificial intelligence methods.

[0077] S24: The simulated GR data of the new well being drilled while being drilled and the input data set are used as inputs of the horizontal section intelligent guidance judgment model, and the probability distribution of the specific depth of the drill bit of the new well being drilled is output.

[0078] The probability distribution of the specific depth of the drill bit refers to the probability of the position of the drill bit relative to the top and bottom interfaces of the target formation, thereby obtaining the specific depth of the wellbore trajectory, thereby determining the position of the drill bit in the target formation in real time.

[0079] The posterior probability distribution of the specific depth φ of the wellbore trajectory is calculated by the following formula:

[0080]

[0081] in, represents the probability distribution of a specific depth φ, i represents the i-th measurement point in the well, γ represents the GR data while drilling, j represents the j-th point in the depth domain corresponding to the i-th measurement point, f ( ) represents the probability density function (such as the likelihood function), and p represents the prior distribution probability. The entire denominator is a normalization constant and is usually not calculated, and the numerator is the unnormalized posterior distribution.

[0082] S25: Take the maximum value in the probability distribution as the predicted depth of the drill bit for the new well.

[0083] In this step, the maximum probability of the different probability distribution points corresponding to the same depth calculated above is taken as the predicted depth of the horizontal wellbore trajectory. , the specific calculation formula is shown in the following formula:

[0084]

[0085] Where ε represents independent and identically distributed random noise with zero mean.

[0086] The above method can be used to obtain the relative position of the wellbore trajectory in the target formation and display it in the form of a heat map. Figure 2 To show the final prediction results with a single well as the reference well, Figure 3 Results are presented for the final prediction using two wells as reference wells.

[0087] S3: judging whether an abnormality occurs during the drilling of the new well according to the predicted depth of the drill bit of the new well and the target formation.

[0088] Specifically, this step includes:

[0089] S31: Determine whether the predicted depth of the drill bit of the new well has penetrated the top interface and the bottom interface of the target formation. If so, the drill bit has penetrated the layer, and it is determined that an abnormality has occurred and an alarm is issued.

[0090] This step determines whether an abnormal situation occurs during the drilling of the new well by predicting the horizontal wellbore trajectory and target formation. If no abnormal situation occurs, drilling continues. If an abnormal situation occurs, an alarm is issued.

[0091] When it is determined that the drill bit has penetrated a layer, an alarm will be issued in time for manual review by the on-site engineer. If the drill bit has indeed penetrated a layer after manual review, the point will be marked. If the drill bit has not penetrated a layer, the point will be regarded as an invalid warning.

[0092] S32: Incorporate the data corresponding to the point where the abnormality occurs into the original data, and update the original data.

[0093] In this step, the manual review results are used as reference points and the corresponding data are included in the original data set, providing an important reference for the next step of model updating.

[0094] Furthermore, the embodiment of the present invention also includes:

[0095] S4: The 3D geological model is updated in real time through updated raw data.

[0096] This step updates the three-dimensional geological model in real time based on the real-time access data, optimizes the entire prediction model, and improves the prediction accuracy.

[0097] The present invention first collects information such as GR while drilling and drilling time according to the target block data, establishes a 3D geological model, and derives the X, Y, Z of the drilled wells in the entire target block, GR while drilling, drilling time data, lithology logging data, etc., as well as the X, Y, Z corresponding to the established geological model, which are used as the model training data set. Secondly, the GR data during the drilling process of the target well is obtained by simulating the reference well and geological model data, and the relative position of the horizontal section of the target wellbore in the formation is predicted by an artificial intelligence method algorithm. Finally, the horizontal section wellbore trajectory prediction model of the target well is used to determine whether the abnormal situation of the drill bit coming out of the layer will occur during the subsequent drilling process, and an early warning is given. At the same time, with the addition of new drilling data, the established 3D geological model can be updated in a timely manner, thereby realizing the continuous optimization of the prediction model.

[0098] The present invention constructs a horizontal well trajectory prediction model during drilling based on artificial intelligence methods, overcomes the drill bit positioning problem during CBM horizontal well drilling, and solves the problems of not being able to find the drilled-out layer in time and automatically adjust the drill bit during horizontal well drilling. It greatly improves the CBM horizontal well drilling efficiency and reservoir drilling rate, significantly improves the single well production and recovery rate of complex oil and gas reservoirs, and reduces on-site construction costs. In addition, it reduces the energy and resource waste caused by correction drilling or shutdown due to the drill bit drilling out of the layer, making the drilling and mining of oil and natural gas more green, energy-saving, low-carbon, environmentally friendly and efficient.

[0099] The embodiment of the present invention also provides a horizontal well guidance abnormality warning device, such as Figure 4 As shown, the device comprises:

[0100] The data acquisition module 1 is used to obtain the three-dimensional position coordinate data, GR data while drilling, drilling time data and lithology logging data of the drilled wells in the entire target block as input data sets.

[0101] Prediction module 2 is used to simulate the GR while drilling data of a new well in the target block based on the GR while drilling data of several wells that have been drilled in the target block, and obtain the predicted depth of the drill bit of the new well based on the simulated GR while drilling data of the new well and the input data set through the established horizontal section intelligent guidance judgment model.

[0102] The early warning module 3 is used to determine whether an abnormality occurs during the drilling process of the new well according to the predicted depth of the drill bit of the new well and the target formation.

[0103] As an example, the aforementioned input data set is obtained through the following process:

[0104] The original data acquisition unit is used to obtain the GR data, drilling time data and lithology logging data of the wells drilled in the target block according to the original data collected on site in the target block.

[0105] The three-dimensional geological model building unit is used to load the sorted GR data while drilling, drilling time data and lithology logging data into the three-dimensional geological modeling software to build a three-dimensional geological model of the target block.

[0106] The input data set export unit is used to export the three-dimensional coordinate data of the geographical location of the drilled wells in the entire target block, the GR data while drilling, the drilling time data, the lithology logging data, the established three-dimensional geological model and the three-dimensional coordinate data of the geographical location of the drilled wells in the three-dimensional geological model according to the set accuracy to obtain the input data set.

[0107] As an improvement of the embodiment of the present invention, the aforementioned prediction module includes:

[0108] The reference well prediction unit is used to take several nearest drilled wells around the new well in the target block as reference wells.

[0109] The GR simulation unit is used to use the while-drilling GR data of a part of the well section of the reference well as the GR value of the corresponding well section of the new well, so as to simulate and obtain the while-drilling GR data of the new well.

[0110] The model building unit is used to build a horizontal section intelligent steering judgment model based on the GR data of the new well obtained by simulation.

[0111] The probability distribution prediction unit is used to use the simulated GR data of the new well and the input data set as the input of the horizontal section intelligent guidance judgment model, and output the probability distribution of the specific depth of the drill bit of the new well.

[0112] The predicted depth determination unit is used to take the maximum value in the probability distribution as the predicted depth of the drill bit for the new drilling.

[0113] Among them, the probability distribution of the specific depth of the drill bit of the new drilling can be calculated by the following formula:

[0114]

[0115] In the above formula, represents the probability distribution of a specific depth φ, i represents the i-th measurement point in the well, γ represents the GR data while drilling, j represents the j-th point in the depth domain corresponding to the i-th measurement point, f ( ) represents the probability density function, and p represents the prior distribution probability.

[0116] Specifically, the early warning module includes:

[0117] The early warning unit is used to determine whether the predicted depth of the drill bit of the new well has penetrated the top and bottom interfaces of the target formation. If so, the drill bit has penetrated the layer, and an abnormality is determined and an alarm is issued.

[0118] The data updating unit is used to incorporate the data corresponding to the point where the abnormality occurs into the original data and update the original data.

[0119] Furthermore, the device of the present invention also includes:

[0120] The model updating module is used to update the three-dimensional geological model in real time through the updated original data.

[0121] The present invention constructs a horizontal wellbore trajectory prediction model during the drilling process based on an artificial intelligence method, which overcomes the problem of drill bit positioning during the drilling of coalbed methane horizontal wells, and solves the problems of being unable to timely detect the drilled layer and automatically adjust the drill bit during the drilling of horizontal wells. It greatly improves the drilling efficiency and reservoir drilling rate of coalbed methane horizontal wells, significantly improves the single well production and recovery rate of complex oil and gas reservoirs, and reduces on-site construction costs.

[0122] The device provided in the above embodiment, its implementation principle and the technical effect produced correspond to the embodiment of the above method one by one. For the sake of brief description, the part not mentioned in the embodiment of the device can refer to the corresponding content in the embodiment of the above method. Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working process of the modules and units described in the device can refer to the corresponding process in the embodiment of the above method, and will not be repeated here.

[0123] The horizontal well steering abnormality warning method described in the above embodiment provided by the present invention can implement business logic through a computer program and record it on a storage medium, and the storage medium can be read and executed by a computer to achieve the effect of the scheme described in the method embodiment of this specification. Therefore, the embodiment of the present invention also provides a computer-readable storage medium for horizontal well steering abnormality warning, including a memory for storing processor-executable instructions, and when the instructions are executed by the processor, the steps of the horizontal well steering abnormality warning method including the above embodiment are implemented.

[0124] The storage medium may include a physical device for storing information, which is usually a medium that digitizes the information and then stores it in an electrical, magnetic or optical manner. The storage medium may include: a device that stores information in an electrical energy manner, such as various memories, such as RAM, ROM, etc.; a device that stores information in a magnetic energy manner, such as a hard disk, a floppy disk, a magnetic tape, a magnetic core memory, a magnetic bubble memory, a USB flash drive; a device that stores information in an optical manner, such as a CD or a DVD. Of course, there are other readable storage media, such as quantum memory, graphene memory, etc.

[0125] The storage medium described above may also include other implementation methods according to the description of the method embodiment. The implementation principle and technical effects produced by this embodiment are the same as those of the aforementioned method embodiment. For details, please refer to the description of the relevant method embodiment, and no further description will be given here.

[0126] The embodiment of the present invention also provides a device for warning of abnormal horizontal well guidance, which may be a separate computer, or may include an actual operating device using one or more methods or one or more embodiments of the present specification. The device for warning of abnormal horizontal well guidance may include at least one processor and a memory storing computer executable instructions, and when the processor executes the instructions, any one or more steps of the above-mentioned method for warning of abnormal horizontal well guidance are implemented.

[0127] The device described above may also include other implementation methods according to the description of the method embodiment. The implementation principle and technical effects produced by this embodiment are the same as those of the aforementioned method embodiment. For details, please refer to the description of the relevant method embodiment, and no further description will be given here.

[0128] Finally, it should be noted that the above-described embodiments are only specific implementations of the present invention, which are used to illustrate the technical solutions of the present invention, rather than to limit them. The protection scope of the present invention is not limited thereto. Although the present invention is described in detail with reference to the above-described embodiments, ordinary technicians in the field should understand that any technician familiar with the technical field can still modify the technical solutions recorded in the above-described embodiments within the technical scope disclosed by the present invention, or can easily think of changes, or make equivalent replacements for some of the technical features therein; and these modifications, changes or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention. They should all be covered within the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A horizontal well steering abnormality early warning method, characterized in that: The method comprises: Obtain the three-dimensional position coordinate data, GR data while drilling, drilling time data and lithology logging data of the drilled wells in the entire target block as input data sets; Simulating the GR while drilling data of a newly drilled well in the target block based on the GR while drilling data of several wells that have been drilled in the target block, and obtaining the predicted depth of the drill bit of the newly drilled well based on the simulated GR while drilling data of the newly drilled well and the input data set through the established horizontal section intelligent guidance judgment model; Based on the predicted depth of the drill bit of the new well and the target formation, it is determined whether an abnormality occurs during the drilling process of the new well.

2. The horizontal well steering abnormality early warning method according to claim 1 is characterized in that: The input data set is obtained by the following method: Based on the original data collected on-site in the target block, the GR data, drilling time data and lithology logging data of the wells drilled in the target block are sorted out; Load the sorted GR data, drilling time data and lithology logging data into the 3D geological modeling software to establish a 3D geological model of the target block; The three-dimensional coordinate data of the geographical location of the drilled wells in the entire target block, the GR data while drilling, the drilling time data, the lithology logging data, the established three-dimensional geological model and the three-dimensional coordinate data of the geographical location of the drilled wells in the three-dimensional geological model are exported according to the set accuracy to obtain the input data set.

3. The horizontal well steering abnormality early warning method according to claim 2 is characterized in that: The method of simulating the GR while drilling data of a newly drilled well in the target block based on the GR while drilling data of several wells that have been drilled in the target block includes: Several wells drilled nearest to the new well in the target block are used as reference wells; The GR data of a part of the well section of the reference well while drilling is used as the GR value of the corresponding well section of the new well, and the GR data of the new well while drilling is simulated.

4. The horizontal well steering abnormality early warning method according to claim 3 is characterized in that: The predicted depth of the drill bit of the new well is obtained by using the established horizontal section intelligent guidance judgment model based on the GR data of the new well obtained by simulation and the input data set, including: According to the GR data of the new well obtained by simulation, the horizontal section intelligent steering judgment model is established; The simulated GR data of the newly drilled well and the input data set are used as inputs of the horizontal section intelligent guidance judgment model to output the probability distribution of the specific depth of the drill bit of the newly drilled well; The maximum value in the probability distribution is taken as the predicted depth of the drill bit for the new well.

5. The horizontal well steering abnormality early warning method according to claim 4 is characterized in that: The probability distribution of the specific depth of the drill bit of the newly drilled well is calculated by the following formula: in, represents the probability distribution of a specific depth φ, i represents the i-th measurement point in the well, γ represents the GR data while drilling, j represents the j-th point in the depth domain corresponding to the i-th measurement point, f ( ) represents the probability density function, and p represents the prior distribution probability.

6. The horizontal well steering abnormality early warning method according to claim 5 is characterized in that: The method of judging whether an abnormality occurs during the drilling of the new well according to the predicted depth of the drill bit of the new well and the target formation obtained includes: Determine whether the predicted depth of the drill bit of the new well has penetrated the top interface and the bottom interface of the target formation. If so, the drill bit has penetrated the formation, and an abnormality is determined and an alarm is issued; The data corresponding to the abnormal point is included in the original data, and the original data is updated.

7. The horizontal well steering abnormality early warning method according to claim 6, characterized in that: The method further comprises: The three-dimensional geological model is updated in real time through updated original data.

8. A horizontal well guidance abnormality warning device, characterized in that: The device comprises: The data acquisition module is used to obtain the three-dimensional position coordinate data, GR data while drilling, drilling time data and lithology logging data of the drilled wells in the entire target block as input data sets; A prediction module is used to simulate the GR while drilling data of a new well in the target block based on the GR while drilling data of several wells that have been drilled in the target block, and obtain the predicted depth of the drill bit of the new well based on the simulated GR while drilling data of the new well and the input data set through the established horizontal section intelligent guidance judgment model; The early warning module is used to determine whether an abnormality occurs during the drilling process of the new well according to the predicted depth of the drill bit of the new well and the target formation.

9. A computer-readable storage medium for horizontal well guidance abnormality warning, characterized in that: It comprises a memory for storing processor executable instructions, and when the instructions are executed by the processor, the steps of the horizontal well steering abnormality early warning method according to any one of claims 1-7 are implemented.

10. A device for warning abnormality of horizontal well guidance, characterized in that: The method comprises at least one processor and a memory storing computer executable instructions, wherein when the processor executes the instructions, the steps of the horizontal well steering abnormality warning method described in any one of claims 1 to 7 are implemented.

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