Well logging while drilling curve prediction method and device based on multiple adjacent wells

Abstract

The invention provides a logging while drilling curve prediction method and device based on multiple adjacent wells, and relates to the field of petroleum drilling, and the method comprises the steps: obtaining actual drilling data of adjacent wells and design trajectory data of a to-be-drilled horizontal well; selecting a reference well combination of each well section of the to-be-drilled horizontal well according to predetermined regional geologic features of the to-be-drilled horizontal well; constructing a guiding model of the horizontal well to be drilled according to the actual drilling data of the adjacent well and the reference well combination of each well section; and generating a logging while drilling curve according to the position of the design trajectory data in the guiding model. According to the method, the prediction precision of the logging-while-drilling curve can be improved, reliable data are provided for guiding analysis, accurate control over the well track is achieved, and the oil reservoir exploitation efficiency is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of oil drilling, and particularly relates to a multi-adjacent-well-based logging-while-drilling curve prediction method and device. BACKGROUND

[0002] In the process of oil drilling, the drilling-while-geosteering technology plays a crucial role. The main purpose is to adjust the well trajectory in real time according to the geological information during drilling, so as to ensure that the drill bit can accurately drill along the predetermined target interval, thereby improving the efficiency and recovery rate of oil reservoir exploitation. Drilling-while-geosteering analysis usually covers a series of steps such as pre-drilling geological comprehensive analysis, steering modeling, drilling-while-steering tracking analysis, and well trajectory optimization adjustment.

[0003] In the key link of steering modeling, the common practice at present is to select a pilot well or an adjacent well as a reference well. With the help of the relevant data such as target interval and logging curve provided by this adjacent well, a steering model is constructed, and further forward modeling prediction of the logging-while-drilling curve is carried out. This single-adjacent-well-based steering modeling method can meet the steering needs in the drilling process to some extent, and has been widely used in early oil drilling operations.

[0004] For example, CN120687728A, a method for establishing a drilling-while-geosteering data set for deep learning. It establishes a drilling-while-geosteering data set, stores the drilling-while-geosteering data set to a geological data center, establishes a drilling-while-geosteering data set, and reduces the difficulty of data processing. CN108442882B, a drilling-while-geosteering method for shale gas large displacement horizontal wells based on two-dimensional seismic data. It performs fine tracking and comparison on the target interval, implements the target area structure, adjusts the well trajectory, and realizes the horizontal section geosteering.

[0005] However, with the continuous development of oil exploitation technology and the increasing demand for oil reservoir exploitation efficiency, this method gradually exposes many shortcomings. The most prominent problem is that as the horizontal well section extends, the distance between its position and the reference well increases, at which time the accuracy of the logging curve simulation prediction will be significantly reduced. This is because the geological information represented by a single adjacent well has limitations. As the distance increases, the geological conditions may change greatly, and the model established based on single adjacent well data cannot accurately reflect these changes, resulting in a decrease in prediction accuracy. In addition, the horizontal well may pass through a fault during drilling. The existence of the fault makes the geological structure complex, and the lithology, physical properties and other parameters of the formation may change abruptly. When the horizontal well passes through the fault, the logging curve prediction method based on a single adjacent well is more severely affected, and the prediction accuracy will further decrease sharply. This makes it impossible to provide accurate and reliable data support for steering analysis during actual drilling, which may cause the well trajectory to deviate from the predetermined target layer, increase drilling costs, reduce oil reservoir exploitation efficiency, and even cause drilling failure and other serious consequences.

[0006] In summary, the existing logging curve prediction method while drilling based on a single adjacent well has a large defect in accuracy and cannot meet the needs of today's complex and changing oil drilling environment. A new method is urgently needed to improve the accuracy of logging curve prediction while drilling and provide more scientific data support for steering analysis.

[0007] This section is intended to provide background or context to the embodiments of the application recited in the claims. The description herein does not admit to be prior art by inclusion in this section. SUMMARY

[0008] To solve the above technical problems, the present application provides the following technical solutions:

[0009] To solve the above technical problems, the present application provides the following technical solutions:

[0010] In a first aspect, the present application provides a logging curve prediction method while drilling based on multiple adjacent wells, comprising:

[0011] Obtaining adjacent well actual drilling data and design trajectory data of a horizontal well to be drilled;

[0012] Selecting a reference well combination of each well section of the horizontal well to be drilled according to the pre-determined regional geological characteristics of the horizontal well to be drilled;

[0013] Building a steering model of the horizontal well to be drilled according to the adjacent well actual drilling data and the reference well combination of each well section;

[0014] generate a logging-while-drilling curve according to the position of the design trajectory data in the steering model.

[0015] Further, the reference well combination of each well section of the horizontal well to be drilled is selected according to a predetermined regional geological feature of the horizontal well to be drilled, comprising:

[0016] The horizontal well to be drilled is divided into well sections according to the design trajectory data and a geological feature of a neighboring well in the drilled data of the neighboring well; wherein the drilled data of the neighboring well comprises logging data of multiple neighboring wells;

[0017] The geological feature corresponding to each well section is determined by comparing the geological features of the neighboring wells; wherein the geological feature comprises layering data and fault position;

[0018] The reference well combination corresponding to the fault of each well section of the horizontal well to be drilled is determined according to the spatial distance between the horizontal well to be drilled and each neighboring well, the regional geological feature of the horizontal well to be drilled, and the geological feature of each neighboring well.

[0019] Further, the steering model comprises a steering framework model; the steering model of the horizontal well to be drilled is constructed according to the drilled data of the neighboring well and the reference well combination of each well section, comprising:

[0020] The steering framework model corresponding to each well section is constructed according to the reference well combination of each well section, the design trajectory data of the horizontal well to be drilled, the obtained stratum deposition rule and stratum structure feature, the position data of the neighboring well in the drilled data of the neighboring well, and the layering data of the neighboring well;

[0021] The steering framework models corresponding to each well section are fused to obtain the steering framework model of the horizontal well to be drilled.

[0022] Further, the steering model comprises a steering attribute model; the steering model of the horizontal well to be drilled is constructed according to the drilled data of the neighboring well and the reference well combination of each well section, comprising:

[0023] The logging curve of the neighboring well in the drilled data of the neighboring well corresponding to the reference well combination is processed into a square wave;

[0024] The stratum attribute of the steering framework model is valued according to the logging curve of the neighboring well after the square wave processing, to obtain the steering attribute model corresponding to the steering framework model.

[0025] Further, the logging-while-drilling curve is generated according to the position of the design trajectory data in the steering model, comprising:

[0026] The design trajectory data is embedded into the steering model according to the position of the design trajectory data in the steering model, to obtain a theoretical logging curve corresponding to the design trajectory data.

[0027] According to each theoretical logging curve and a corresponding weight, a logging-while-drilling curve is generated.

[0028] Further, the logging-while-drilling curve prediction method based on multiple adjacent wells further comprises:

[0029] In the actual drilling process of the horizontal well to be drilled, real-time drilling trajectory data and real-time drilling logging curves are acquired;

[0030] According to the real-time position of the real-time drilling trajectory data in the steering model, real-time drilling theoretical logging curves are generated;

[0031] The real-time drilling theoretical logging curves and the real-time drilling logging curves are compared;

[0032] According to the comparison result, the steering model is adjusted in real time.

[0033] In a second aspect, the present application provides a logging-while-drilling curve prediction device based on multiple adjacent wells, comprising:

[0034] A data acquisition unit is configured to acquire adjacent well drilling data and design trajectory data of a horizontal well to be drilled;

[0035] A reference combination selection unit is configured to select reference well combinations of each well section of the horizontal well to be drilled according to pre-determined regional geological features of the horizontal well to be drilled;

[0036] A steering model construction unit is configured to construct a steering model of the horizontal well to be drilled according to the adjacent well drilling data and the reference well combinations of each well section;

[0037] A logging curve generation unit is configured to generate a logging-while-drilling curve according to the position of the design trajectory data in the steering model.

[0038] Further, the reference combination selection unit comprises:

[0039] A well section division module is configured to divide the horizontal well to be drilled into each well section according to the design trajectory data and adjacent well geological features in the adjacent well drilling data; wherein the adjacent well drilling data comprises logging data of multiple adjacent wells;

[0040] A geological feature determination module is configured to determine corresponding geological features of each well section by comparing the adjacent well geological features; wherein the geological features comprise layering data and fault positions;

[0041] A reference combination determination module is configured to determine reference well combinations corresponding to faults of each well section of the horizontal well to be drilled according to spatial distances between the horizontal well to be drilled and each adjacent well, regional geological features of the horizontal well to be drilled, and adjacent well geological features of each adjacent well.

[0042] Further, the guide model comprises a guide framework model; the guide model construction unit comprises:

[0043] The framework model construction module is configured to construct a guide framework model corresponding to each well section according to the reference well combination of each well section, the design trajectory data of the horizontal well to be drilled, the obtained stratum deposition rule and stratum structure feature, the well position data and the layered data of the adjacent well in the adjacent well drilling data.

[0044] The framework model fusion module is configured to fuse the guide framework models corresponding to each well section to obtain the guide framework model of the horizontal well to be drilled.

[0045] Further, the guide model comprises a guide attribute model; the guide model construction unit comprises:

[0046] The square wave processing module is configured to perform square wave processing on the well logging curve in the adjacent well drilling data corresponding to the reference well combination.

[0047] The attribute model construction module is configured to perform stratum attribute assignment on the guide framework model according to the well logging curve after square wave processing to obtain the guide attribute model corresponding to the guide framework model.

[0048] Further, the well logging curve generation unit comprises:

[0049] The theoretical curve generation module is configured to embed the design trajectory data in the guide model according to the position of the design trajectory data in the guide model to obtain a theoretical well logging curve corresponding to the design trajectory data.

[0050] The while-drilling curve generation module is configured to generate a while-drilling well logging curve according to each theoretical well logging curve and the corresponding weight.

[0051] Further, the multi-adjacent-well-based while-drilling well logging curve prediction method further comprises:

[0052] The drilling trajectory curve acquisition unit is configured to acquire real-time drilling trajectory data and real-time drilling well logging curve in the actual drilling process of the horizontal well to be drilled.

[0053] The drilling theoretical curve generation unit is configured to generate a drilling theoretical well logging curve according to the real-time position of the drilling trajectory data in the guide model.

[0054] The well logging curve comparison unit is configured to compare the drilling theoretical well logging curve and the real-time drilling well logging curve.

[0055] The model real-time adjustment unit is configured to perform real-time adjustment on the guide model according to the comparison result.

[0056] In a third aspect, the present application provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method for predicting a while-drilling logging curve based on multiple adjacent wells when executing the program.

[0057] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the method for predicting a while-drilling logging curve based on multiple adjacent wells.

[0058] In a fifth aspect, the present application provides a computer program product comprising computer programs / instructions, wherein the computer programs / instructions, when executed by a processor, implement the steps of the method for predicting a while-drilling logging curve based on multiple adjacent wells.

[0059] To solve the problem of low prediction accuracy of a while-drilling steering curve in the prior art, the method and device for predicting a while-drilling logging curve based on multiple adjacent wells provided by the present application can solve the problem of low prediction accuracy of a while-drilling steering curve in the prior art. In the steering modeling stage, multiple adjacent well drilling data are collected, and the design trajectory data of the horizontal well to be drilled are combined to divide well sections; for different well sections and fault positions, reference well combinations are accurately selected; by constructing a steering framework model and a steering attribute model of the horizontal well to be drilled, a weighted algorithm is used to predict a while-drilling logging curve before drilling, and the steering framework model and the steering attribute model are adjusted during actual drilling to adjust the while-drilling logging curve in real time, to provide reliable data for steering analysis, to realize accurate control of a wellbore drilling trajectory, to improve reservoir exploitation efficiency, to enhance adaptability to complex geological conditions, and to ensure safe and efficient drilling operations. BRIEF DESCRIPTION OF DRAWINGS

[0060] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed in the embodiments or the prior art description will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative effort.

[0061] Figure 1 The flowchart of the method for predicting a while-drilling logging curve based on multiple adjacent wells in the embodiments of the present application;

[0062] Figure 2 The flowchart of selecting a reference well combination in the embodiments of the present application;

[0063] Figure 3 The flowchart of constructing a steering framework model in the embodiments of the present application;

[0064] Figure 4A flow chart for constructing a steering attribute model in an embodiment of the present application;

[0065] Figure 5 A flow chart for generating a logging-while-drilling curve in an embodiment of the present application;

[0066] Figure 6 A flow chart for real-time adjustment of a logging-while-drilling curve in an embodiment of the present application;

[0067] Figure 7 A structure diagram of a logging-while-drilling curve prediction device based on multiple adjacent wells in an embodiment of the present application;

[0068] Figure 8 A structure diagram of a reference combination selection unit in an embodiment of the present application;

[0069] Figure 9 A structure diagram of a steering model construction unit in an embodiment of the present application;

[0070] Figure 10 A structure diagram of a steering model construction unit in an embodiment of the present application;

[0071] Figure 11 A structure diagram of a logging curve generation unit in an embodiment of the present application;

[0072] Figure 12 A structure diagram of a logging-while-drilling curve prediction device based on multiple adjacent wells in an embodiment of the present application;

[0073] Figure 13 A structure diagram of an electronic device in an embodiment of the present application;

[0074] Figure 14 A logging-while-steering curve prediction flow chart based on multiple adjacent wells in an embodiment of the present application;

[0075] Figure 15 A plane structure analysis diagram in an embodiment of the present application;

[0076] Figure 16 A three-dimensional space diagram of a horizontal well and adjacent wells in an embodiment of the present application;

[0077] Figure 17 A reference well multiple well comparison diagram in an embodiment of the present application;

[0078] Figure 18 A target well and reference well profile diagram in an embodiment of the present application;

[0079] Figure 19 A steering framework model in an embodiment of the present application;

[0080] Figure 20 A steering framework modeling parameter setting interface diagram in an embodiment of the present application;

[0081] Figure 21 is an attribute model graph (Rt) in the embodiments of the present application;

[0082] Figure 22 is a schematic diagram of a logging-while-drilling curve prediction in the embodiments of the present application. DETAILED DESCRIPTION

[0083] To make the objectives, technical solutions and advantages of the embodiments of the present application clearer, further detailed description will be made to the embodiments of the present application in combination with the drawings. Herein, the illustrative embodiments of the present application and the description thereof are used to explain the present application, but not as a limitation to the present application.

[0084] The information collected in the technical solutions in the present application is information and data authorized by users or authorized by all parties, and the collection, storage, use, processing, transmission, provision, disclosure and application of the related data all comply with the relevant laws, regulations and standards of the relevant countries and regions, take necessary security measures, do not violate public order and good customs, and provide corresponding operation portals for users to choose authorization or refusal.

[0085] The corresponding operation portals are provided for the users to choose to agree or refuse the automatic decision result; if the user chooses to refuse, the expert decision process is entered.

[0086] In an embodiment, referring to Figure 1 In order to improve the prediction accuracy of logging-while-drilling curves, provide reliable data for steering analysis, realize accurate control of well trajectory, and improve oil reservoir exploitation efficiency, the present application provides a logging-while-drilling curve prediction method based on multiple adjacent wells, comprising:

[0087] S101: acquiring adjacent well actual drilling data and design trajectory data of a horizontal well to be drilled; wherein the adjacent well actual drilling data comprises adjacent well trajectory data, layered data and logging data;

[0088] S102: selecting a reference well combination of each well section of the horizontal well to be drilled according to a pre-determined regional geological feature of the horizontal well to be drilled;

[0089] S103: constructing a steering model of the horizontal well to be drilled according to the adjacent well actual drilling data and the reference well combination of each well section;

[0090] S104: generating a logging-while-drilling curve according to the position of the design trajectory data in the steering model.

[0091] It can be understood that the embodiments of the present application abandon the traditional single adjacent well reference mode, and determine to use multiple adjacent wells as reference in the steering modeling stage. In the early stage of steering modeling, the real drilling data of multiple adjacent wells in the target area are comprehensively collected and analyzed in depth. These adjacent well real drilling data include but are not limited to the target interval of the adjacent well, the logging curve, the geological layering information and the formation lithology data, etc. Through comprehensive comparison and research on these rich data, and combined with the design trajectory data of the to-be-drilled horizontal well, the geological feature change situation of different well sections and faults of the to-be-drilled horizontal well in the drilling process is predicted in advance.

[0092] Specifically, according to the design trajectory of the horizontal well, for different well sections of the horizontal well, the reference well combination of each well section is determined according to the relative position relationship with each adjacent well and the geological similarity of the corresponding area of each adjacent well. For example, in the starting section of the horizontal well, two or more adjacent wells with high matching degree of geological features and close distance to the starting position are selected as the reference wells of this section. Through detailed analysis and integration of the logging curve characteristics, formation parameters and other data of these adjacent wells in the starting area, a steering model suitable for the starting section is established, which is used for the prediction of the logging while drilling curve.

[0093] With the extension of the horizontal well section, for different well sections in the subsequent, the corresponding reference well combination is also determined in the steering modeling stage according to the spatial position relationship with each adjacent well and the change of geological conditions. For the area that may pass through the fault, the data of multiple adjacent wells around the fault are analyzed in detail. According to the type (such as normal fault, reverse fault, etc.), size and intersection angle of the fault, and other information of the horizontal well and the fault, the reference well combination used in different positions of the fault is accurately determined in the modeling stage. For example, for a normal fault, specific adjacent wells near the hanging wall of the fault are selected as the main reference, and the relevant data of other adjacent wells are matched, and another group of reference well combinations are selected according to the specific geological conditions near the foot wall, so as to accurately predict the change of the logging while drilling curve when passing through the fault.

[0094] The embodiments of the present application comprehensively and carefully determine different reference well combinations (including two or more adjacent wells) used in different horizontal well sections and fault positions in the steering modeling stage, aiming to solve the problem that the prediction accuracy is affected by the distance between the horizontal well and the reference well and the fault in the prior art, improve the accuracy of the logging while drilling curve prediction, and provide more scientific and accurate data support for steering analysis, so that the well trajectory can be drilled along the target interval more accurately, and the efficiency and quality of oil drilling operation are effectively improved.

[0095] In summary, referring to Figure 14The method for predicting a drilling guiding curve based on multiple adjacent wells provided by the application can improve the prediction accuracy of the drilling guiding curve, provide reliable data for guiding analysis, realize accurate control of a wellbore trajectory, improve reservoir exploitation efficiency, enhance adaptability to complex geological conditions, ensure safe and efficient drilling operation, and promote the technical progress of the petroleum drilling industry.

[0096] As can be known from the above description, the method for predicting a drilling guiding curve based on multiple adjacent wells provided by the application can solve the problem of low prediction accuracy of a drilling guiding curve in the petroleum drilling process under the traditional single-adjacent-well reference mode. In the guiding modeling stage, the drilling data of multiple adjacent wells are collected, and the well sections are divided in combination with the design trajectory data of the horizontal well to be drilled. The reference well combination is accurately selected for different well sections and fault positions. The guiding framework model and the guiding attribute model of the horizontal well to be drilled are constructed, the weighted algorithm is used to predict the drilling logging curve before drilling, and the guiding framework model and the guiding attribute model are adjusted in the actual drilling process to adjust the drilling logging curve in real time, thereby providing reliable data for guiding analysis, realizing accurate control of the wellbore drilling trajectory, improving reservoir exploitation efficiency, enhancing adaptability to complex geological conditions, and ensuring safe and efficient drilling operation.

[0097] In an embodiment, referring to Figure 2 The reference well combination of each well section of the horizontal well to be drilled is selected according to the pre-determined regional geological characteristics of the horizontal well to be drilled, and includes:

[0098] S201: dividing the horizontal well to be drilled into well sections according to the design trajectory data and the adjacent well geological characteristics in the adjacent well drilling data; wherein the adjacent well drilling data include the logging data of multiple adjacent wells;

[0099] S202: determining the geological characteristics corresponding to each well section by comparing the adjacent well geological characteristics; wherein the geological characteristics include fault positions;

[0100] S203: determining the reference well combination corresponding to the fault of each well section of the horizontal well to be drilled according to the spatial distance between the horizontal well to be drilled and each adjacent well, the regional geological characteristics of the horizontal well to be drilled, and the adjacent well geological characteristics of each adjacent well.

[0101] It can be understood that first, various types of real drilling data of multiple adjacent wells and design trajectory data of the to-be-drilled horizontal well are collected, then the to-be-drilled horizontal well is segmented according to the design trajectory and geological information; then the reference well combination is determined for each segment and different positions of the fault; then the guiding framework model and the attribute model are constructed; finally, the logging curve is predicted or simulated according to the model, the trajectory and the weighted algorithm in the pre-drilling and real drilling stages. The embodiments of the application break through the limitation of the traditional single adjacent well mode, greatly improve the prediction accuracy, provide accurate data for guiding analysis, and effectively prevent the deviation of the well trajectory.

[0102] In the face of complex geological conditions, especially in the presence of geological faults, the use of multiple adjacent well data enables it to better cope with it and ensure smooth drilling. At the same time, the geological risks are detected in advance, which effectively guarantees the safety of personnel and equipment.

[0103] The specific steps of selecting the reference well combination of each well segment and fault of the to-be-drilled horizontal well include:

[0104] Step one: data collection and arrangement.

[0105] Before guiding modeling, the real drilling data of multiple adjacent wells in the target area is first collected. The sources of these adjacent well drilling data include geological exploration reports, logging data records, etc. of adjacent wells. The specific data types cover the target layer information of adjacent wells, such as the depth range, lithological characteristics, etc. of each layer; logging curve data, such as resistivity curve, acoustic time difference curve, natural gamma curve, etc. These curves can reflect the electrical, acoustic and radioactive characteristics of the formation; in addition, there are stratified data of the formation, which accurately record the thickness, boundary position, etc. of each layer. At the same time, the design trajectory data of the to-be-drilled horizontal well is obtained, including wellhead coordinates, designed inclination angle, azimuth angle and expected horizontal section length, etc. to prepare for subsequent analysis.

[0106] Step two: horizontal well segment division and geological feature pre-judgment.

[0107] According to the design trajectory of the to-be-drilled horizontal well, combined with the collected geological information of adjacent wells, the entire drilling process of the to-be-drilled horizontal well is divided into multiple different well segments. For example, according to a certain length interval, such as 100 meters for each well segment, or according to the change nodes of geological structure. For each well segment, by comparing the geological data of adjacent wells at the corresponding position, the possible geological features encountered by the well segment are pre-judged, such as the lithological change trend of the formation, the possible formation dip angle, etc. For the area expected to pass through the fault, the position range of the fault should be further accurately defined, and the nature (normal fault, reverse fault, etc.) and size of the fault should be inferred according to the adjacent well data.

[0108] Step three: reference well combination determination.

[0109] For each divided horizontal well section and different positions of the fault under each well section, the reference well combination is determined according to the geological feature prediction result. The specific method is that, for a certain horizontal well section, the spatial distance between each adjacent well and the well section is calculated, and the similarity degree of the geological features of the adjacent wells in the corresponding area is compared. The similarity degree is determined according to various factors, including lithology similarity, stratum dip angle consistency, matching degree of logging curve shape, etc. For example, in a certain well section, if the lithology of adjacent well A is closest to the predicted lithology of the well section, and the logging curve shape of adjacent well A matches the geological features of the area with high degree, and adjacent well A is close to the well section, then adjacent well A can be used as the main reference well. At the same time, adjacent well B and adjacent well C which have complementary geological parameters with adjacent well A are selected as auxiliary reference wells, forming the reference well combination of the well section.

[0110] For the fault area, the reference well is selected according to the nature of the fault and the relative position relationship between the horizontal well and the fault. For example, on the hanging wall of the normal fault, the adjacent well with close distance to the hanging wall and consistent geological features with the hanging wall area is selected as the main reference well, and another suitable adjacent well is selected for the foot wall, to ensure accurate reference basis at different positions of the fault.

[0111] As can be seen from the above description, the multi-adjacent-well-based logging-while-drilling curve prediction method provided by the application can select the reference well combination of each well section of the horizontal well to be drilled according to the pre-determined regional geological features of the horizontal well to be drilled.

[0112] In an embodiment, referring to Figure 3 The guidance model includes a guidance framework model; and the guidance model of the horizontal well to be drilled is constructed according to the adjacent well drilling data and the reference well combination of each well section, including:

[0113] S301: A guidance framework model corresponding to each well section is constructed according to the reference well combination of each well section, the design trajectory data of the horizontal well to be drilled, the obtained stratum deposition rule and stratum structure feature, the adjacent well position data in the adjacent well drilling data, and the layering data of the adjacent well;

[0114] S302: The guidance framework models corresponding to each well section are fused to obtain the guidance framework model of the horizontal well to be drilled.

[0115] It can be understood that according to the determined reference well combination of different positions of each well section and fault, a steering framework model is established for each region by using a geological modeling software. In the modeling process, the logging curve data, geological layering data and the like of the reference well are integrated according to a preset mathematical algorithm and a geological rule. For example, the logging curve data of the reference well is extended to the corresponding region of the to-be-drilled horizontal well by an interpolation algorithm, and a three-dimensional steering framework model reflecting the geological characteristics of the region is constructed in combination with the sedimentary rule and the structural characteristics of the stratum. In the model, the spatial distribution of each horizon, the lithological change and the mutual relationship between the strata and the like are determined.

[0116] From the above description, it can be known that the method for predicting a logging curve while drilling provided in the application can construct a steering model of the to-be-drilled horizontal well according to the real drilling data of the adjacent well and the reference well combination of each well section.

[0117] In an embodiment, referring to Figure 4 , the steering model comprises a steering attribute model; and the method for constructing the steering model of the to-be-drilled horizontal well according to the real drilling data of the adjacent well and the reference well combination of each well section comprises:

[0118] S401: performing square wave processing on the adjacent well logging curve in the real drilling data of the adjacent well corresponding to the reference well combination;

[0119] S402: performing stratum attribute assignment on the steering framework model according to the adjacent well logging curve after the square wave processing, to obtain a steering attribute model corresponding to the steering framework model.

[0120] It can be understood that in the steering modeling process, according to the positional relationship between the target well and the adjacent well, a plurality of adjacent wells are selected as reference wells, a target layer or a stratum structure within a certain range above the target layer is taken as a constraint condition, a steering framework model is constructed, a steering attribute model is established by taking the logging curve of the adjacent well as a reference, and a theoretical logging curve is established in combination with the shape and position of the designed trajectory. The specific steps comprise:

[0121] 1) square wave processing of the adjacent well logging curve: first, one or more logging curves (usually gamma logging, resistivity logging, acoustic logging, density logging and the like) of the adjacent well are subjected to low-pass filtering to remove high-frequency noise and highlight the main trend; then, the inflection points of the logging curve are extracted, and the impedance values between adjacent inflection points are set as the weighted average values of the corresponding intervals, so that the curve assumes a square wave shape.

[0122] 2) taking the target layer and the adjacent horizon as the horizon of the steering model, taking one or more horizons corresponding to the structural surface as a constraint condition, and setting the other horizons as one of the three modes of proportional, parallel top and planar bottom according to the need, to construct a three-dimensional steering framework model.

[0123] 3) Constructing the guiding attribute model: using the data of the neighboring well logging curve after square wave processing, assigning values according to the corresponding relationship of the guiding framework model, obtaining the formation attribute model of the guiding framework model; according to the type and quantity of the selected neighboring well logging curve, different types and quantities of guiding attribute models can be obtained; different neighboring wells can be selected as reference wells, combined with the distance of different well sections and different reference wells, using different reference wells and their logging curves, the guiding attribute model of different well sections can be obtained.

[0124] 4) Logging-while-drilling curve prediction: according to the position of the designed trajectory data of the horizontal well to be drilled in the guiding framework model, the data in the range is weighted and integrated according to the detection range of different logging curves, so as to obtain the predicted logging curve corresponding to the designed trajectory data.

[0125] From the above description, it can be known that the logging-while-drilling curve prediction method based on multiple neighboring wells provided by the application can construct the guiding model of the horizontal well to be drilled according to the real drilling data of the neighboring wells and the reference well combination of each well section.

[0126] In an embodiment, referring to Figure 5 , the logging-while-drilling curve generated according to the position of the designed trajectory data in the guiding model comprises:

[0127] S501: embedding the designed trajectory data into the guiding model according to the position of the designed trajectory data in the guiding model, to obtain the theoretical logging curve corresponding to the designed trajectory data;

[0128] S502: generating a logging-while-drilling curve according to each theoretical logging curve and the corresponding weight.

[0129] It can be understood that, based on the established guiding framework model, first, the position of the designed trajectory data of the horizontal well to be drilled in the guiding framework model is determined. Combined with different reference well data, horizontal well section conditions and corresponding logging curves of each section, a guiding attribute model is further established. When establishing the guiding attribute model, the geological feature weight of different reference wells in each well section is fully considered, for example, reference wells with high geological feature similarity and short distance are given higher weight.

[0130] Then, according to the position of the horizontal well design trajectory data in the steering framework model and the measurement range of different logging curves, different logging curves are predicted by a weighting algorithm. Specifically, for each kind of logging curve (such as a resistivity curve), the resistivity curve data of each reference well is weighted and calculated according to the weight of the corresponding data of each reference well in the steering attribute model. For example, if adjacent wells A, B and C are reference wells of a well section, the weights are w1, w2 and w3 respectively, and the corresponding resistivity curve data are R1, R2 and R3 respectively, then the predicted resistivity curve data R of the well section is R = w1×R1 + w2×R2 + w3×R3. In this way, different types of logging curves are calculated, thereby completing the prediction of the pre-drilling logging-while-drilling curve (also known as the theoretical logging curve) and providing theoretical data support for the drilling operation.

[0131] From the above description, it can be known that the method for predicting a logging-while-drilling curve based on multiple adjacent wells provided by the application can generate a logging-while-drilling curve according to the position of the design trajectory data in the steering model.

[0132] In an embodiment, referring to Figure 6 , the method for predicting a logging-while-drilling curve based on multiple adjacent wells further comprises:

[0133] S601: In the actual drilling process of a horizontal well to be drilled, real-time drilling trajectory data and real-time drilling logging curve data are acquired;

[0134] S602: A real-time drilling theoretical logging curve is generated according to the real-time position of the real-time drilling trajectory data in the steering model;

[0135] S603: The real-time drilling theoretical logging curve and the real-time drilling logging curve are compared;

[0136] S604: The steering model is adjusted in real time according to the comparison result.

[0137] It can be understood that in the actual drilling process, real-time drilling trajectory data and logging-while-drilling data in the drilling process are acquired in real time. Similar to the pre-drilling prediction, first, the real-time position of the real-time drilling trajectory in the steering framework model is determined. Then, the steering attribute model established before drilling and the real-time geological condition feedback (such as real-time monitoring of formation pressure change, lithology change obtained by cutting analysis, etc.) in the drilling process are combined to adjust the steering attribute model in real time.

[0138] According to the position of the real drilling trajectory in the steering frame model and the measurement range of different logging curves, the logging-while-drilling curve of the real drilling trajectory is simulated and predicted through a weighting algorithm. In the weighting calculation process, the weight of each reference well data is dynamically adjusted according to the real-time feedback of the geological conditions. For example, if it is found in the real drilling process that the lithology of a certain region is more similar to that of a certain section of well A, the weight of well A in the prediction calculation of this region is appropriately increased. Through continuous adjustment of the weight according to the real drilling situation and weighting calculation, accurate simulation and prediction of the logging-while-drilling curve of the real drilling trajectory are realized, accurate data support is provided for real-time steering analysis, so as to timely adjust the well trajectory and ensure that the horizontal well accurately drills along the target horizon.

[0139] From the above description, it can be seen that the logging-while-drilling curve prediction method based on multiple adjacent wells provided by the application can realize real-time adjustment of the steering model.

[0140] A specific embodiment is given below.

[0141] The shale reservoir is taken as an example for illustration in the embodiment of the application. The geological data of the block are relatively rich. At present, 6 straight wells or inclined straight wells have been drilled, and detailed analysis has been carried out. The stratified data, interpretation conclusions, lithology and other data are formed. At the same time, the target layer structure data are collected. The steering modeling and logging-while-drilling steering curve prediction of the well are completed by using the logging-while-drilling steering curve prediction method based on multiple adjacent wells established by the application.

[0142] Step 1: data collection and arrangement.

[0143] Referring to Figure 15 , according to the position of the target well and the depth of the target layer, according to the well position of the target well T3 and the depth of the target layer A721, the coordinates, well trajectory, logging curves (gamma Gr, resistivity Rt), interpretation conclusions, stratified data, etc. of six straight wells or inclined straight wells of Cai44, Xi206, P9, Yue62, QT9, T3 pilot eye wells are collected.

[0144] Step 2: reference well combination establishment.

[0145] Referring to Figure 16 , the data collected above is superimposed and displayed in a three-dimensional graph to show the spatial topological structure, as shown in the following figure. It can be seen that different positions of the target well are close to Cai44, pilot eye well and QT9 well.

[0146] Referring to Figure 17 , from the comparison of multiple wells, the depth and thickness of the target layer of the three reference wells are not consistent. Therefore, if only one well is used as a reference, the steering model and the predicted logging-while-drilling curve will have unreasonable places.

[0147] Further, referring toFigure 18 , cut a profile in the longitudinal direction along the target well trajectory, and project the above three adjacent well data onto the profile, and superimpose the actual logging curve Gr, it can be clearly seen that along the target well, at different horizontal positions, it is closer to different reference wells, so the above three wells are selected as the reference well combination for guided modeling.

[0148] Step 3: Guided framework modeling.

[0149] According to the above analysis and the established reference well combination, the Gr and Rt logging curves of the three reference wells Cai44, the pilot hole well and QT9 are selected, and the layered data A62, A63, A711, A712, A721, A722 and A73 are selected. Since there is no fault in the region, according to the different distances of the three reference wells at different positions of the horizontal section, the horizontal section is divided into three sections, the first section (0-150m) takes Cai44 well as the reference well, the second section (150-1200m) takes the pilot hole well as the reference well, and the third section (1200m after) takes QT9 as the reference well. The guided framework model is shown in Figure 19 . In addition, the guided framework modeling parameter setting interface is shown in Figure 20 .

[0150] Step 4: Attribute modeling.

[0151] For the three regions, the logging curves Gr and Rt of the corresponding reference wells are used to establish three attribute models Gr attribute and Rt attribute respectively, and the results are shown in Figure 21 .

[0152] Step 5: Logging while drilling curve prediction.

[0153] According to the attribute model, the position of the well trajectory in the guided model and the detection radius of the logging while drilling tool, the detection radius of the resistivity Rt is set to 5 inches, and the detection radius of the gamma Gr is set to 8 inches. The logging while drilling gamma and resistivity logging curves simulated by the method are shown in Figure 22 .

[0154] In summary, the logging while drilling guided curve prediction method based on multiple adjacent wells has brought many significant beneficial effects:

[0155] Firstly, in terms of prediction accuracy, by considering the data of multiple adjacent wells in the steering modeling stage and carefully selecting reference well combinations for different horizontal well sections and fault positions, the prediction accuracy problem caused by the increasing distance between the horizontal well and the reference well and the crossing of the fault under the traditional single-adjacent-well reference mode is overcome. Before drilling, the steering attribute model is established using the data of multiple adjacent wells, and the well logging curve is predicted through a weighting algorithm, which fully integrates the geological information advantages of multiple adjacent wells in different regions and can more accurately reflect the geological characteristics and well logging curve changes at different positions of the horizontal well compared to single-adjacent-well prediction. During drilling, the steering attribute model and the weight are dynamically adjusted based on real-time feedback, further ensuring the accuracy of the prediction. This greatly improves the while-drilling well logging curve prediction accuracy, provides more reliable data support for steering analysis, and effectively avoids well trajectory deviation caused by prediction errors, improving reservoir exploitation efficiency.

[0156] Secondly, from the perspective of cost control, accurate while-drilling well logging curve prediction helps to reduce unnecessary drilling footage and correction operations. Under the traditional method, due to limited prediction accuracy, the well trajectory may need to be adjusted multiple times, increasing drilling time and cost. However, the present invention can more accurately guide drilling, reducing the probability of repeated drilling, sidetracking, and other operations due to trajectory deviation, saving a lot of manpower, material resources, and time cost. For example, reducing a sidetracking operation caused by trajectory deviation can save several days of drilling time and hundreds of thousands of yuan in costs, greatly improving the economic benefits of drilling operations.

[0157] Thirdly, in terms of dealing with complex geological conditions, the present method has stronger adaptability. Petroleum drilling often faces complex geological structures such as faults and fractures. By analyzing the data of multiple adjacent wells around the fault and selecting appropriate reference wells for different fault positions, the present invention can better predict the changes in well logging curves when the horizontal well crosses the fault. Even when other complex geological conditions are encountered, the comprehensive use of multiple adjacent well data can provide a more comprehensive perspective for geological analysis, helping drilling personnel more accurately grasp the underground geological structure and develop countermeasures in advance to ensure the smooth progress of drilling operations.

[0158] In addition, the method of the present invention also improves the safety of drilling operations. Accurate while-drilling well logging curve prediction can help drilling personnel discover potential geological risks such as abnormal formation pressure and lithology changes in a timely manner. Early warning of these risks allows drilling personnel to take appropriate measures such as adjusting drilling parameters and optimizing drilling fluid performance to avoid accidents such as blowouts and sticking caused by unexpected geological conditions, ensuring the safety of drilling site workers and the normal operation of equipment.

[0159] Finally, from the overall oil exploitation industry development point of view, the application of the method helps to promote the industry technology progress. The innovative multi-adjacent well reference mode and the accurate prediction algorithm provide a new idea and method for the while-drilling steering technology, which can be used as the basis for the improvement and development of other related technologies, promote the improvement of the whole oil drilling field technology level, and improve the oil resource exploitation efficiency and competitiveness.

[0160] Based on the same inventive concept, the embodiment of the present application also provides a multi-adjacent well based while-drilling logging curve prediction device, which can be used to realize the method described in the above embodiments, as described in the following embodiments. Since the multi-adjacent well based while-drilling logging curve prediction device solves the problem by the same principle as the multi-adjacent well based while-drilling logging curve prediction method, the implementation of the multi-adjacent well based while-drilling logging curve prediction device can be referred to the implementation of the software performance benchmark determination method, and the repeated parts will not be described here. The term "unit" or "module" used below can be a combination of software and / or hardware that realizes a predetermined function. Although the system described in the following embodiments is preferably realized in software, the realization of hardware or a combination of software and hardware is also possible and conceived.

[0161] In an embodiment, referring to Figure 7 In order to improve the while-drilling logging curve prediction accuracy, provide reliable data for steering analysis, realize well trajectory accurate control, and improve oil reservoir exploitation efficiency, the present application provides a multi-adjacent well based while-drilling logging curve prediction device, which comprises: a data acquisition unit 701, a reference combination selection unit 702, a steering model construction unit 703, and a logging curve generation unit 704.

[0162] The data acquisition unit 701 is used to acquire adjacent well actual drilling data and design trajectory data of a to-be-drilled horizontal well.

[0163] The reference combination selection unit 702 is used to select a reference well combination of each well section of the to-be-drilled horizontal well according to a pre-determined regional geological feature of the to-be-drilled horizontal well.

[0164] The steering model construction unit 703 is used to construct a steering model of the to-be-drilled horizontal well according to the adjacent well actual drilling data and the reference well combination of each well section.

[0165] The logging curve generation unit 704 is used to generate a while-drilling logging curve according to the position of the design trajectory data in the steering model.

[0166] In an embodiment, referring to Figure 8 The reference combination selection unit 702 comprises: a well section division module 801, a geological feature determination module 802, and a reference combination determination module 803.

[0167] The well section division module 801 is configured to divide the horizontal well to be drilled into well sections according to the design trajectory data and the adjacent well geological features in the adjacent well real drilling data; wherein the adjacent well real drilling data comprises logging data of multiple adjacent wells.

[0168] The geological feature determination module 802 is configured to determine the geological features corresponding to each well section by comparing the adjacent well geological features; wherein the geological features comprise layering data, fault position.

[0169] The reference combination determination module 803 is configured to determine the reference well combination corresponding to the fault of each well section of the horizontal well to be drilled according to the spatial distance between the horizontal well to be drilled and each adjacent well, the regional geological features of the horizontal well to be drilled and the adjacent well geological features of each adjacent well.

[0170] In an embodiment, referring to Figure 9 , the guide model comprises a guide framework model; and the guide model construction unit 703 comprises a framework model construction module 901 and a framework model fusion module 902.

[0171] The framework model construction module 901 is configured to construct the guide framework model corresponding to each well section according to the reference well combination of each well section, the design trajectory data of the horizontal well to be drilled, the obtained stratum deposition rule and stratum structure feature, the adjacent well position data in the adjacent well real drilling data and the layering data of the adjacent well.

[0172] The framework model fusion module 902 is configured to fuse the guide framework models corresponding to each well section to obtain the guide framework model of the horizontal well to be drilled.

[0173] In an embodiment, referring to Figure 10 , the guide model comprises a guide attribute model; and the guide model construction unit 703 comprises a square wave processing module 1001 and an attribute model construction module 1002.

[0174] The square wave processing module 1001 is configured to perform square wave processing on the adjacent well logging curve in the adjacent well real drilling data corresponding to the reference well combination.

[0175] The attribute model construction module 1002 is configured to perform stratum attribute assignment on the guide framework model according to the square wave processed adjacent well logging curve to obtain the guide attribute model corresponding to the guide framework model.

[0176] In an embodiment, referring to Figure 11 , the logging curve generation unit 704 comprises a theoretical curve generation module 1101 and a while-drilling curve generation module 1102.

[0177] The theoretical curve generation module 1101 is configured to embed the design trajectory data into the steering model according to a position of the design trajectory data in the steering model, to obtain a theoretical logging curve corresponding to the design trajectory data.

[0178] The while-drilling curve generation module 1102 is configured to generate a while-drilling logging curve according to each theoretical logging curve and a corresponding weight.

[0179] In an embodiment, referring to Figure 12 The method for predicting a while-drilling logging curve based on multiple adjacent wells further includes an actual drilling trajectory curve acquisition unit 1201, an actual drilling theoretical curve generation unit 1202, a logging curve comparison unit 1203, and a model real-time adjustment unit 1204.

[0180] The actual drilling trajectory curve acquisition unit 1201 is configured to acquire actual drilling trajectory data and an actual drilling logging curve in real time during an actual drilling process of a horizontal well to be drilled.

[0181] The actual drilling theoretical curve generation unit 1202 is configured to generate an actual drilling theoretical logging curve according to a real-time position of the actual drilling trajectory data in the steering model.

[0182] The logging curve comparison unit 1203 is configured to compare the actual drilling theoretical logging curve and the actual drilling logging curve.

[0183] The model real-time adjustment unit 1204 is configured to adjust the steering model in real time according to a comparison result.

[0184] From a hardware perspective, in order to improve the prediction accuracy of a while-drilling logging curve, provide reliable data for steering analysis, realize accurate control of a wellbore trajectory, and improve oil reservoir exploitation efficiency, an embodiment of an electronic device for implementing all or part of the method for predicting a while-drilling logging curve based on multiple adjacent wells is provided, and the electronic device specifically includes the following content:

[0185] A processor (Processor), a memory (Memory), a communications interface (Communications Interface), and a bus; wherein the processor, the memory, and the communications interface communicate with each other through the bus; the communications interface is configured to implement information transmission between the device for predicting a while-drilling logging curve based on multiple adjacent wells and a core business system, a user terminal, and a related database and other related devices; the logic controller can be a desktop computer, a tablet computer, a mobile terminal, and the like, and the embodiment is not limited thereto. In the embodiment, the logic controller can be implemented by referring to the embodiments of the method for predicting a while-drilling logging curve based on multiple adjacent wells and the embodiments of the device for predicting a while-drilling logging curve based on multiple adjacent wells, the contents of which are incorporated herein, and repeated descriptions are omitted.

[0186] It can be understood that the user terminal can include a smart phone, a tablet electronic device, a network set-top box, a portable computer, a desktop computer, a personal digital assistant (PDA), a vehicle-mounted device, a smart wearable device, and the like. The smart wearable device can include smart glasses, a smart watch, a smart bracelet, and the like.

[0187] In actual application, part of the method for predicting a logging-while-drilling curve based on multiple adjacent wells can be executed on an electronic device as described above, or all operations can be completed in the client device. Specifically, the selection can be made according to the processing capability of the client device and the restriction of a user's use scenario, and the like. The present application does not limit this. If all operations are completed in the client device, the client device can further include a processor.

[0188] The client device described above can have a communication module (i.e., a communication unit) and can be communicatively connected with a remote server to realize data transmission with the server. The server can include a server of a task scheduling center side, and can further include a server of an intermediate platform in other implementation scenarios, such as a server of a third-party server platform communicatively connected with the server of the task scheduling center. The server can include a single computer device, a server cluster composed of multiple servers, or a server structure of a distributed device.

[0189] Figure 13 A schematic block diagram of a system configuration of an electronic device 9600 of an embodiment of the present application is shown in FIG. 9. As shown in FIG. 9, the electronic device 9600 can include a central processing unit 9100 and a memory 9140; the memory 9140 is coupled to the central processing unit 9100. It is noted that the configuration shown in FIG. 9 is exemplary; other types of structures can also be used to supplement or replace the structure to realize telecommunication functions or other functions. Figure 13 Figure 13 It is noted that the configuration shown in FIG. 9 is exemplary; other types of structures can also be used to supplement or replace the structure to realize telecommunication functions or other functions.

[0190] In an embodiment, the function of the method for predicting a logging-while-drilling curve based on multiple adjacent wells can be integrated into the central processing unit 9100. The central processing unit 9100 can be configured to perform the following control:

[0191] S101: acquiring real drilling data of adjacent wells and design trajectory data of a horizontal well to be drilled;

[0192] S102: selecting a reference well combination of each well section of the horizontal well to be drilled according to a pre-determined regional geological feature of the horizontal well to be drilled;

[0193] S103: constructing a steering model of the horizontal well to be drilled according to the real drilling data of the adjacent wells and the reference well combination of each well section; ​

[0194] S104: generating a logging-while-drilling curve according to the position of the design trajectory data in the steering model.

[0195] From the above description, the multi-adjacent well-based logging-while-drilling curve prediction method provided by the application can solve the problem of low prediction accuracy of the drilling steering curve in the traditional single-adjacent well reference mode in the prediction of the drilling steering curve of oil drilling. In the steering modeling stage, the actual drilling data of multiple adjacent wells are collected, and the design trajectory data of the horizontal well to be drilled are combined to divide the well sections; for different well sections and fault positions, the reference well combination is accurately selected; by constructing the steering framework model and the steering attribute model of the horizontal well to be drilled, the weighted algorithm is used to predict the logging-while-drilling curve before drilling, and the steering framework model and the steering attribute model are adjusted in the actual drilling process to adjust the logging-while-drilling curve in real time, provide reliable data for steering analysis, realize accurate control of the wellbore drilling trajectory, improve the oil reservoir exploitation efficiency, enhance the adaptability to complex geological conditions, and ensure the safe and efficient drilling operation.

[0196] In another embodiment, the multi-adjacent well-based logging-while-drilling curve prediction device can be configured separately from the central processor 9100, for example, the data composite transmission device based on the multi-adjacent well-based logging-while-drilling curve prediction device can be configured as a chip connected with the central processor 9100, and the function of the multi-adjacent well-based logging-while-drilling curve prediction method is realized through the control of the central processor.

[0197] As shown in Figure 13 , the electronic device 9600 can also include a communication module 9110, an input unit 9120, an audio processor 9130, a display 9160, and a power supply 9170. It should be noted that the electronic device 9600 does not necessarily include all the components shown in Figure 13 ; in addition, the electronic device 9600 can also include components not shown in Figure 13 , which can be referred to the prior art.

[0198] As shown in Figure 13 , the central processor 9100, also known as a controller or operation control, can include a microprocessor or other processor device and / or logic device, which receives input and controls the operation of each component of the electronic device 9600.

[0199] The memory 9140, for example, can be one or more of a buffer, a flash memory, a hard drive, a removable media, a volatile memory, a non-volatile memory, or other suitable device. The information related to the failure can be stored, and the program for executing the related information can also be stored. The central processor 9100 can execute the program stored in the memory 9140 to realize information storage or processing, etc.

[0200] The input unit 9120 provides input to the central processing unit 9100. The input unit 9120 is, for example, a key or a touch input device. The power supply 9170 is for supplying power to the electronic device 9600. The display 9160 is for displaying display objects such as images and characters. The display is, for example, an LCD display, but is not limited thereto.

[0201] The memory 9140 can be a solid-state memory such as a read only memory (ROM), a random access memory (RAM), a SIM card, and the like. It can also be a memory that retains information even when power is off, can be selectively erased, and is provided with more data, an example of which is sometimes referred to as an EPROM or the like. The memory 9140 can also be some other type of device. The memory 9140 includes a buffer memory 9141 (sometimes referred to as a buffer). The memory 9140 can include an application / function storage section 9142 for storing application programs and function programs or for storing a flow for executing the operation of the electronic device 9600 by the central processing unit 9100.

[0202] The memory 9140 can also include a data storage section 9143 for storing data such as contacts, digital data, pictures, sounds, and / or any other data used by the electronic device. A driver storage section 9144 of the memory 9140 can include various drivers of the electronic device for a communication function and / or for executing other functions of the electronic device such as a messaging application, an address book application, and the like.

[0203] The communication module 9110 is a transmitter / receiver that transmits and receives signals via an antenna 9111. The communication module (transmitter / receiver) 9110 is coupled to the central processing unit 9100 to provide input signals and receive output signals, which can be the same as in the case of a conventional mobile communication terminal.

[0204] Based on different communication technologies, a plurality of communication modules 9110 can be provided in the same electronic device, such as a cellular network module, a Bluetooth module, and / or a wireless local area network module, and the like. The communication module (transmitter / receiver) 9110 is also coupled to a speaker 9131 and a microphone 9132 via an audio processor 9130 to provide audio output via the speaker 9131 and receive audio input from the microphone 9132, thereby implementing a conventional telecommunication function. The audio processor 9130 can include any suitable buffer, decoder, amplifier, and the like. In addition, the audio processor 9130 is also coupled to the central processing unit 9100, thereby enabling recording on the local device through the microphone 9132 and enabling playing of a sound stored on the local device through the speaker 9131.

[0205] The embodiment of the present application further provides a computer readable storage medium capable of realizing all steps of the multi-adjacent-well-based while-drilling curve prediction method with the execution subject being a server or a client in the above-described embodiment, and the computer program is stored on the computer readable storage medium, and when the computer program is executed by a processor, all steps of the multi-adjacent-well-based while-drilling curve prediction method with the execution subject being a server or a client in the above-described embodiment are realized, for example, the following steps are realized when the processor executes the computer program:

[0206] S101: acquiring adjacent well actual drilling data and design trajectory data of a to-be-drilled horizontal well;

[0207] S102: selecting a reference well combination of each well section of the to-be-drilled horizontal well according to a pre-determined regional geological feature of the to-be-drilled horizontal well;

[0208] S103: constructing a steering model of the to-be-drilled horizontal well according to the adjacent well actual drilling data and the reference well combination of each well section;

[0209] S104: generating a while-drilling curve according to a position of the design trajectory data in the steering model.

[0210] As can be known from the above description, the multi-adjacent-well-based while-drilling curve prediction method provided by the present application can solve the problem of low prediction accuracy of a while-drilling steering curve in the traditional single-adjacent-well reference mode in the prediction of the while-drilling steering curve in oil drilling. In the steering modeling stage, the actual drilling data of multiple adjacent wells are collected, and the design trajectory data of the to-be-drilled horizontal well are combined to divide well sections; the reference well combination is accurately selected according to different well sections and fault positions; the steering framework model and the steering attribute model of the to-be-drilled horizontal well are constructed, the weighted algorithm is used to predict the while-drilling curve before drilling, and the steering framework model and the steering attribute model are adjusted in the actual drilling process, so as to adjust the while-drilling curve in real time, provide reliable data for steering analysis, realize accurate control of the wellbore drilling trajectory, improve the oil reservoir exploitation efficiency, enhance the adaptability to complex geological conditions, and ensure that the drilling operation is safely and efficiently performed.

[0211] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a device, or a computer program product. Therefore, the present application can adopt a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer usable program codes.

[0212] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart Figure 1 one or more functions specified in the flowchart or multiple flows and / or blocks. Figure 1 one or more functions specified in the flowchart or multiple flows and / or blocks.

[0213] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including an instruction device that implements the function specified in the flowchart Figure 1 one or more functions specified in the flowchart or multiple flows and / or blocks. Figure 1 one or more functions specified in the flowchart or multiple flows and / or blocks.

[0214] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart Figure 1 one or more functions specified in the flowchart or multiple flows and / or blocks. Figure 1 one or more functions specified in the flowchart or multiple flows and / or blocks.

[0215] The principles and implementation manners of the present application are described in the specific embodiments, and the above embodiment descriptions are only used to help understand the method of the present application and its core idea; meanwhile, for those skilled in the art, according to the idea of the present application, the specific implementation manners and application ranges can be changed, and the above descriptions should not be understood as the limitation of the present application.

Claims

1. A multi-neighbor well-based LWD curve prediction method, characterized in that, The method comprises the following steps: obtaining adjacent well drilling data and design trajectory data of a to-be-drilled horizontal well; selecting a reference well combination of each well section of the to-be-drilled horizontal well according to a predetermined regional geological feature of the to-be-drilled horizontal well; constructing a guiding model of the to-be-drilled horizontal well according to the adjacent well drilling data and the reference well combination of each well section; generating a logging-while-drilling curve according to the position of the design trajectory data in the guiding model.

2. The multi-neighbor well based LWD curve prediction method of claim 1, wherein, The step of selecting the reference well combination of each well section of the to-be-drilled horizontal well according to the predetermined regional geological feature of the to-be-drilled horizontal well comprises the following steps: dividing the to-be-drilled horizontal well into each well section according to the design trajectory data and adjacent well geological features in the adjacent well drilling data; wherein the adjacent well drilling data comprises logging data of multiple adjacent wells; determining the corresponding geological features of each well section by comparing the adjacent well geological features; wherein the geological features comprise layering data and fault position; determining the corresponding reference well combination of each well section of the to-be-drilled horizontal well according to the spatial distance between the to-be-drilled horizontal well and each adjacent well, the regional geological feature of the to-be-drilled horizontal well and the adjacent well geological feature of each adjacent well.

3. The multi-neighbor well based LWD curve prediction method of claim 1, wherein, The guiding model comprises a guiding framework model; the step of constructing the guiding model of the to-be-drilled horizontal well according to the adjacent well drilling data and the reference well combination of each well section comprises the following steps: constructing a corresponding guiding framework model of each well section according to the reference well combination of each well section, the design trajectory data of the to-be-drilled horizontal well, the obtained stratum deposition rule and stratum structure feature, the adjacent well position data and layering data of the adjacent well in the adjacent well drilling data; fusing the corresponding guiding framework model of each well section to obtain the guiding framework model of the to-be-drilled horizontal well.

4. The multi-neighbor well based LWD curve prediction method of claim 3, wherein, The guiding model comprises a guiding attribute model; the step of constructing the guiding model of the to-be-drilled horizontal well according to the adjacent well drilling data and the reference well combination of each well section comprises the following steps: sine-waving the adjacent well logging curve in the adjacent well drilling data corresponding to the reference well combination; assigning stratum attributes to the guiding framework model according to the sine-waved adjacent well logging curve to obtain the guiding attribute model corresponding to the guiding framework model.

5. The multi-neighbor well based LWD curve prediction method of claim 1, wherein, The step of generating a logging-while-drilling curve according to the position of the design trajectory data in the guiding model comprises the following steps: embedding the design trajectory data in the guiding model according to the position of the design trajectory data in the guiding model to obtain a theoretical logging curve corresponding to the design trajectory data; generating a logging-while-drilling curve according to each theoretical logging curve and the corresponding weight.

6. The multi-neighbor well based LWD curve prediction method of claim 1, wherein, The method further comprises the following steps: in the actual drilling process of the to-be-drilled horizontal well, obtaining real-time drilling trajectory data and real-time logging curve; generating a real-time drilling theoretical logging curve according to the real-time position of the real-time drilling trajectory data in the guiding model; comparing the real-time drilling theoretical logging curve with the real-time logging curve; adjusting the guiding model in real time according to the comparison result.

7. A multi-neighbor well-based LWD curve prediction device, characterized in that, The method comprises the following steps: a data obtaining unit, configured to obtain adjacent well drilling data and design trajectory data of a to-be-drilled horizontal well; a reference combination selecting unit, configured to select a reference well combination of each well section of the to-be-drilled horizontal well according to a predetermined regional geological feature of the to-be-drilled horizontal well; a guide model construction unit, configured to construct a guide model of the horizontal well to be drilled according to the real drilling data of the adjacent well and the reference well combination of each well section; a logging curve generation unit, configured to generate a logging while drilling curve according to the position of the design trajectory data in the guide model.

8. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor executes the program to implement the steps of the multi-adjacent-well-based logging while drilling curve prediction method in any one of claims 1 to 6.

9. A computer readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the steps of the multi-adjacent-well-based logging while drilling curve prediction method in any one of claims 1 to 6.

10. A computer program product comprising computer programs / instructions, characterized in that, The computer program / instruction is executed by the processor to implement the steps of the multi-adjacent-well-based logging while drilling curve prediction method in any one of claims 1 to 6.

Images