Guiding method, system and device based on dynamic three-dimensional geological model of logging while drilling and medium

Abstract

The invention discloses a guiding method, a guiding system, a guiding device and a guiding medium based on a dynamic three-dimensional geologic model for logging while drilling. According to the guiding method, the guiding system and the guiding device, a three-dimensional geologic model around a horizontal well is constructed by utilizing conventional logging data of a vertical well around the horizontal well; latest horizontal well drilling and logging data and horizontal well conventional logging-while-drilling data information are supplemented into the model, and a three-dimensional geologic model body is continuously iterated and updated, so that the unification of the three-dimensional geologic model, drilling, logging and logging-while-drilling understanding is realized; the spatial relationship between the current drilling direction and a high-quality reservoir is judged in a mode of slicing along the track of the forward drilling horizontal well, decision data is provided for horizontal well geosteering, and the purpose of improving the drilling rate of an oil layer and a gas layer of the horizontal well is achieved.

Description

Technical Field

[0001] The invention belongs to the technical field of petroleum logging, and relates to a steering method, system, device and medium based on a dynamic three-dimensional geological model of logging while drilling. Background Art

[0002] At present, there are four main ways to geo-steering shale oil and shale gas horizontal wells. The first is to use the surrounding control wells to draw a well-connected profile, artificially connect the oil layer, poor oil layer and other reservoirs according to geological knowledge to form a two-dimensional reservoir well-connected profile, and then add the actual drilling depth, well inclination, and azimuth to the profile, and make guidance suggestions based on the position of the current horizontal well trajectory in the profile. This is a qualitative horizontal well geo-steering method. The second is to use the nearest control well of the horizontal well to divide the top and bottom interfaces of the formation and use natural gamma or resistivity logging curves to invert a two-dimensional profile that can characterize the heterogeneity change characteristics of the reservoir, and then put the actual drilling trajectory into the profile and then forward model a logging curve with the corresponding attributes. By comparing the curve obtained by the horizontal well while drilling logging and the change law of the forward modeled curve, geo-steering suggestions are provided. This is a quantitative horizontal well geo-steering method. The third is to use high-end logging while drilling instruments, such as azimuth gamma imaging while drilling, electromagnetic wave while drilling, and electromagnetic wave edge detection while drilling, to make guidance suggestions by observing the changing characteristics of the logging while drilling curve. The fourth is to use the three-dimensional seismic inversion data body, slice along the horizontal well trajectory to form a profile, intuitively display the position of the horizontal well trajectory in the seismic phase axis, and provide geological guidance suggestions. This is a semi-quantitative analysis method.

[0003] The first method of geosteering based on the well-connected profile of the surrounding control wells has the advantage of fast analysis speed, but the disadvantage is that the surrounding control wells and the horizontal wells are not in the same plane. The changes in the actual formation structure and reservoir thickness will cause the hard-projected control wells to be inconsistent with the reservoir structure and thickness actually encountered by the horizontal wells. The reservoir profile itself is a mean profile that can only express the connectivity of the reservoir, but cannot reflect the changing characteristics of the lithology and oil content inside the reservoir. The latest logging and logging while drilling information cannot be used to update the reservoir profile. There will be a large deviation when using this method for geosteering. The second method of using two-dimensional inversion while drilling profile for geosteering is better than the first method. Its advantages are fast analysis speed and ability to reflect the characteristics of lithology and oil content changes in the reservoir. However, its disadvantage is that since the control well and the horizontal well are usually not in the same plane, the control well projected onto the steering profile will hard-constrain the generation of the two-dimensional steering profile with the reservoir thickness and structural position of the control well, which will have a certain deviation from the actual drilling understanding, resulting in problems with the forward curve. By comparing this forward curve with the logging while drilling curve and adjusting the structure, it is easy to amplify the error caused by the change in reservoir thickness, affecting the decision of geosteering. The third method relies on high-end logging while drilling instruments. Due to the limitation of instrument cost, the price of field service is high, which is not conducive to the efficient development of shale oil and shale gas. It is often only used in some key wells and complex wells. The fourth method of using three-dimensional seismic slice guidance must collect three-dimensional seismic before drilling, which is costly and has a long seismic data processing cycle. The inversion profile is limited by the vertical resolution of the seismic data itself, and cannot well describe the characteristics of lithology and oil content changes in thin reservoirs. Summary of the invention

[0004] The purpose of the present invention is to solve the problems in the prior art that the connected well profile drawn by the peripheral control wells cannot reflect the changing characteristics of the lithology and oil content inside the reservoir; the latest logging and logging while drilling information cannot be used to update the reservoir profile; the two-dimensional inversion of the drilling while drilling profile for geological guidance is due to the fact that the control wells and the horizontal wells are usually not in the same plane, which leads to problems in the forward curve, and it is easy to amplify the error caused by the change of reservoir thickness, affecting the geological guidance decision. The field service price of high-end logging while drilling instruments is high, which is not conducive to the efficient development of shale oil and shale gas; the use of three-dimensional seismic slicing guidance method must collect three-dimensional seismic before drilling, which is costly and has a long seismic data processing cycle. The inversion profile is limited by the vertical resolution of the seismic data itself and cannot well depict the changing characteristics of the lithology and oil content inside the thinner reservoir. A guidance method, system, device and medium based on the dynamic three-dimensional geological model of logging while drilling are provided.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] The steering method based on the dynamic 3D geological model of logging while drilling includes:

[0007] S1: Compare and divide the logging data of the control wells around the horizontal well into small layers;

[0008] S2: Based on the results of the sub-layer division, a 3D structural model of the target area is established; reservoir lithology identification and lithofacies division are carried out, and the lithofacies model is established under the control of the 3D structural model of the target area by discretizing the lithofacies curve; based on the logging curves of the horizontal wells and the lithofacies model, an initial 3D geological attribute model of the work area is established;

[0009] S3: Input the logging data of the horizontal wells into the initial three-dimensional geological attribute model of the work area, and convert the logging data of the horizontal wells into lithofacies codes corresponding to the reservoir and non-reservoir. The discretized data and the lithofacies data of the surrounding wells are used in lithofacies modeling operations to form an iteratively updated lithofacies model.

[0010] S4: Discretize the logging curve of the current horizontal well, and perform phase-controlled three-dimensional attribute modeling with the logging curves of the surrounding control wells under the control of the lithofacies model to form an iteratively updated three-dimensional geological model;

[0011] S5: Cut the 3D logging attribute model slice corresponding to the drilling curve along the current horizontal well trajectory, analyze the spatial position of the horizontal well trajectory in the reservoir, and provide the well deviation data required for geosteering decision-making;

[0012] S6: After receiving the new logging and LWD data, repeat steps S3 to S5 to implement geosteering by iteratively updating the 3D model using the logging and LWD data.

[0013] A further improvement of the present invention is:

[0014] Furthermore, the logging data of the control wells around the horizontal wells are compared and divided into small layers. Specifically, the well selection range of the control wells is the wells within the range of a vertical well extrapolated from the vertical wells around the horizontal wells; the small layer comparison is carried out using the methods of equal formation thickness, sedimentary cycles and marker layers, and the well-connected profiles for small layer comparison must be closed.

[0015] Furthermore, based on the results of the sub-layer division, a three-dimensional structural model of the target area is established; and reservoir lithology identification and lithofacies division are carried out, and the lithofacies curves are discretized to establish the lithofacies model under the control of the three-dimensional structural model of the target area; based on the logging curves and lithofacies model of the horizontal well, an initial three-dimensional geological attribute model of the work area is established, specifically: based on the results of the sub-layer division and the geological modeling software, a three-dimensional structural model of the target area is constructed, the reservoir interpreted by logging is taken as the reservoir-like lithofacies, and the non-reservoir section is taken as the mudstone lithofacies, and the lithofacies code is discretized, and under the constraint of the three-dimensional structural model of the target area, the lithofacies model is generated by interpolation through the sequential indicator interpolation algorithm; under the premise of lithofacies control, a logging attribute model is established, and the porosity, oil saturation, permeability and resistivity of the mudstone phase are assigned; the attribute model of the sandstone phase adopts the sequential Gaussian random simulation interpolation algorithm to establish the initial three-dimensional geological attribute model of the work area of ​​natural gamma, resistivity, mud content, porosity, saturation and permeability under the premise of lithofacies control.

[0016] Furthermore, the logging data of the horizontal well includes: current depth data of the horizontal well, trajectory data, lithologic logging data and gamma data while drilling; the horizontal well logging data is converted into lithofacies codes corresponding to reservoirs and non-reservoir layers, and the discretized data and the lithofacies data of surrounding wells are used in lithofacies modeling operations to form an iteratively updated lithofacies model, specifically: constructing an iterative lithofacies model by means of pre-set interpolation algorithms and variogram parameters, automatically calling the latest logging lithologic data and the lithologic data of surrounding wells for lithologic modeling.

[0017] Furthermore, the logging curves of the horizontal wells include gamma while drilling and resistivity while drilling logging curves; before the logging curves of the horizontal wells are discretized and the phase-controlled three-dimensional attribute modeling is performed with the logging curves of the surrounding control wells under the control of the lithofacies model, it also includes: the gamma while drilling, resistivity while drilling logging curves and the logging curves of the surrounding control wells are standardized by the trend surface method or the marker layer average correction method;

[0018] The discretized current horizontal well logging curve is used to perform phase-controlled three-dimensional attribute modeling with the logging curves of the surrounding control wells under the control of the lithofacies model. Specifically, the latest logging curve while drilling and the logging curves of the surrounding wells are called to participate in the calculation through the interpolation algorithm and variation function parameters set in advance, so as to realize the iterative update of the three-dimensional geological model.

[0019] Furthermore, a three-dimensional logging attribute model slice corresponding to the drilling curve is cut along the current horizontal well trajectory, the spatial position of the horizontal well trajectory in the reservoir is analyzed, and the well inclination data required for geological steering decision-making is provided. Specifically, if the trajectory touches the lithology boundary upward, a declination reduction prompt is issued; if it touches the lithology boundary downward, an inclination increase prompt is issued; under the condition of the trajectory inside the reservoir, the curve data of the drill bit position point indicated by the latest trajectory and the straight line range extending 100m while maintaining the current well inclination angle are used as the judgment range. The judgment curve is the natural gamma while drilling. The judgment curve is judged by the three-dimensional model grid value of every 1m in the extension direction and the current natural gamma, as shown in formula (1):

[0020]

[0021] Where R is the rate of change of natural gamma, dimensionless; GR 模型值 , the natural gamma value of the grid corresponding to the current decision point, API; GR while drilling, the current latest natural gamma value while drilling, API;

[0022] The maximum well inclination angle for determining increase or decrease is set manually, with the default setting of 10°. If the change is within 10°, the adjusted well inclination angle data is automatically calculated and submitted to the geological steering decision-making link; if it exceeds 10°, a reminder is issued for manual intervention and judgment.

[0023] The guidance system based on the dynamic 3D geological model of logging while drilling includes:

[0024] A sub-layer comparison module, which performs sub-layer comparison and division on the logging data of the control wells around the horizontal well;

[0025] A construction module, wherein the construction module establishes a three-dimensional structural model of the target area based on the results of the sub-layer division; and performs reservoir lithology identification and lithofacies division, and discretizes the lithofacies curve to establish a lithofacies model under the control of the three-dimensional structural model of the target area; and establishes an initial three-dimensional geological attribute model of the work area based on the logging curves and lithofacies model of the horizontal wells;

[0026] A discretization module, which inputs the logging data of the horizontal wells into the initial three-dimensional geological attribute model of the work area, and converts the logging data of the horizontal wells into lithofacies codes corresponding to the reservoir and non-reservoir. The discretized data and the lithofacies data of the surrounding wells participate in the lithofacies modeling operation to form an iteratively updated lithofacies model;

[0027] A modeling module, wherein the modeling module discretizes the logging curve of the current horizontal well, and performs phase-controlled three-dimensional attribute modeling with the logging curve of the surrounding control wells under the control of the lithofacies model to form an iteratively updated three-dimensional geological model;

[0028] An analysis module, wherein the analysis module cuts a three-dimensional well logging attribute model slice corresponding to the drilling curve along the current horizontal well trajectory, analyzes the spatial position of the horizontal well trajectory in the reservoir, and provides well deviation data required for geosteering decision-making;

[0029] The geosteering module receives new logging and LWD data and implements geosteering by iteratively updating the three-dimensional model using logging and LWD data.

[0030] A terminal device comprises 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 above method when executing the computer program.

[0031] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above method are implemented.

[0032] Compared with the prior art, the present invention has the following beneficial effects:

[0033] The present invention uses conventional logging data of vertical wells around horizontal wells to construct a three-dimensional geological model around horizontal wells. By adding the latest horizontal well drilling logging data and conventional horizontal well logging data to the model, the three-dimensional geological model body is continuously updated iteratively, so as to unify the three-dimensional geological model with the knowledge of drilling, logging, and logging while drilling. By slicing along the trajectory of the horizontal well being drilled and automatically judging the effective well inclination angle according to the set prediction distance and maximum adjustment angle, guidance support is provided to realize intelligent guidance decision-making. By proposing a clear guidance angle through the true three-dimensional model body, the horizontal well trajectory can be kept in the reservoir, the reservoir drilling rate of the horizontal well is improved, and the horizontal well mining efficiency is increased. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0035] Figure 1 A flow chart of the steering method based on the dynamic three-dimensional geological model of logging while drilling of the present invention;

[0036] Figure 2 Flow chart for well site data transmission;

[0037] Figure 3 Flow chart for establishing iterative lithofacies model using real horizontal well logging lithofacies data;

[0038] Figure 4 Flowchart for establishing iterative logging attribute model using LWD data from actual horizontal wells;

[0039] Figure 5 A flow chart for automatically obtaining the well inclination angle;

[0040] Figure 6 It is a structural schematic diagram of the guidance system based on the dynamic three-dimensional geological model of logging while drilling of the present invention;

[0041] Figure 7 Another flow chart of the steering method based on the dynamic three-dimensional geological model of logging while drilling of the present invention;

[0042] Figure 8 This is a schematic diagram of the logging sub-layer division of the surrounding control wells;

[0043] Fig. 9 A schematic diagram of the adjustment trajectory for calling the steering well inclination angle;

[0044] Fig.10 Schematic diagram of the trajectory of the steering well inclination angle adjustment after iterative training. DETAILED DESCRIPTION

[0045] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0046] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0047] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0048] In the description of the embodiments of the present invention, it should be noted that if the terms "upper", "lower", "horizontal", "inner", etc. indicate an orientation or positional relationship based on the orientation or positional relationship shown in the drawings, or the orientation or positional relationship in which the product of the invention is usually placed when in use, it is only for the convenience of describing the present invention and simplifying the description, and does not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention. In addition, the terms "first", "second", etc. are only used to distinguish the description, and cannot be understood as indicating or implying relative importance.

[0049] In addition, if the term "horizontal" appears, it does not mean that the component must be absolutely horizontal, but can be slightly tilted. For example, "horizontal" only means that its direction is more horizontal than "vertical", which does not mean that the structure must be completely horizontal, but can be slightly tilted.

[0050] In the description of the embodiments of the present invention, it is also necessary to explain that, unless otherwise clearly specified and limited, the terms "set", "install", "connect", and "connect" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal connection of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0051] The present invention is further described in detail below in conjunction with the accompanying drawings:

[0052] See also Figure 1 The present invention discloses a steering method based on a dynamic three-dimensional geological model of logging while drilling, comprising:

[0053] S101: Perform sub-layer comparison and division on the logging data of the control wells around the horizontal well.

[0054] The selection range of control wells is the wells within the range of a vertical well extrapolated from the vertical wells around the horizontal well; the sub-layer comparison is carried out using the methods of equal thickness of formations, sedimentary cycles and marker layers, and the well-connected profile of the sub-layer comparison must be closed.

[0055] S102: Based on the results of the sub-layer division, a three-dimensional structural model of the target area is established; reservoir lithology identification and lithofacies division are carried out, and the lithofacies model is established under the control of the three-dimensional structural model of the target area using discretized lithofacies curves; based on the logging curves of the horizontal wells and the lithofacies model, an initial three-dimensional geological attribute model of the work area is established.

[0056] Based on the results of sub-layer division and geological modeling software, a three-dimensional structural model of the target area is constructed. The reservoir interpreted by well logging is regarded as a reservoir-like lithofacies, and the non-reservoir section is regarded as a mudstone lithofacies. The lithofacies code is discretized, and the lithofacies model is generated by interpolation through the sequential indicator interpolation algorithm under the constraint of the three-dimensional structural model of the target area. Under the premise of lithofacies control, a well logging attribute model is established, and the porosity, oil saturation, permeability and resistivity of the mudstone phase are assigned. The attribute model of the sandstone phase adopts the sequential Gaussian random simulation interpolation algorithm to establish the initial three-dimensional geological attribute model of the work area of ​​natural gamma, resistivity, mud content, porosity, saturation and permeability under the premise of lithofacies control.

[0057] S103: Input the logging data of the horizontal wells into the initial three-dimensional geological attribute model of the work area, and convert the logging data of the horizontal wells into lithofacies codes corresponding to the reservoir and non-reservoir. The discretized data and the lithofacies data of the surrounding wells are used in lithofacies modeling operations to form an iteratively updated lithofacies model.

[0058] The logging data of horizontal wells include: current depth data, trajectory data, lithology logging data and gamma data while drilling; the current depth data, trajectory data, lithology logging data, gamma data while drilling and other information of horizontal wells can be transmitted to the transmission database. The modeling computer is connected to the transmission database, and the data is directly called into the modeling computer. See the well site data transmission process for details. Figure 2 .

[0059] The horizontal well logging data is converted into lithofacies codes corresponding to reservoirs and non-reservoir layers. The discretized data and the lithofacies data of surrounding wells are used in lithofacies modeling operations to form an iteratively updated lithofacies model. Specifically:

[0060] The iterative lithofacies model is constructed by automatically calling the latest logging lithology data and the lithology data of surrounding wells for lithology modeling through the pre-set interpolation algorithm and variogram parameters. The process of using the logging lithofacies data of the actual horizontal well to establish the iterative lithofacies model is as follows: Figure 3 shown.

[0061] S104: discretizing the logging curve of the current horizontal well, and performing phase-controlled three-dimensional attribute modeling with the logging curves of the surrounding control wells under the control of the lithofacies model to form an iteratively updated three-dimensional geological model.

[0062] The logging curves of the horizontal wells include gamma while drilling and resistivity while drilling logging curves; before the logging curves of the horizontal wells are discretized and the phase-controlled three-dimensional attribute modeling is performed with the logging curves of the surrounding control wells under the control of the lithofacies model, it also includes: standardizing the gamma while drilling, resistivity while drilling logging curves and the logging curves of the surrounding control wells by using the trend surface method or the marker layer average correction method;

[0063] Discretize the logging curve of the current horizontal well, and perform phase-controlled three-dimensional attribute modeling with the logging curve of the surrounding control wells under the control of the lithofacies model. Specifically, through the pre-set interpolation algorithm and variation function parameters, call the latest logging curve while drilling and the logging curve of the surrounding wells to participate in the calculation, and realize the iterative update of the three-dimensional geological model. The process of using the logging data of the actual horizontal well to establish the iterative logging attribute model is as follows: Figure 4 described.

[0064] S105: Cut a three-dimensional logging attribute model slice corresponding to the drilling curve along the current horizontal well trajectory, analyze the spatial position of the horizontal well trajectory in the reservoir, and provide well deviation data required for geosteering decision-making.

[0065] If the trajectory touches the lithology boundary upward, a declination reduction prompt is issued; if it touches the lithology boundary downward, an declination increase prompt is issued; under the condition of the trajectory inside the reservoir, the curve data of the drill bit position indicated by the latest trajectory and the straight line range extending 100m while maintaining the current well inclination angle are used as the judgment range. The judgment curve is the natural gamma while drilling. The judgment curve is judged by the three-dimensional model grid value of every 1m in the extension direction and the current natural gamma. The judgment process is as follows Figure 5 As shown in formula (1):

[0066]

[0067] Where R is the rate of change of natural gamma, dimensionless; GR 模型值 , the natural gamma value of the grid corresponding to the current decision point, API; GR while drilling, the current latest natural gamma value while drilling, API;

[0068] The maximum inclination angle for judging increase or decrease is set manually, and the default setting is 10°. If the change is within 10°, the adjusted inclination angle data is automatically calculated and submitted to the geological steering decision link; if it exceeds 10°, a reminder is issued for manual intervention. The default judgment distance of the process is 20m, and the qualified standard is 15m. This parameter can be adjusted by technicians according to actual conditions. The commonly used indicators can be adjusted to 20m and 18m as the qualified standard; it can be adjusted to 20m and 16m as the qualified standard. The judgment distance should not exceed 80m.

[0069] S106: after receiving the new logging and LWD data, repeat steps S103 to S105 to implement geosteering by iteratively updating the three-dimensional model using the logging and LWD data.

[0070] See also Figure 6 The present invention discloses a guidance system based on a dynamic three-dimensional geological model of logging while drilling, comprising:

[0071] A sub-layer comparison module, which performs sub-layer comparison and division on the logging data of the control wells around the horizontal well;

[0072] A construction module, wherein the construction module establishes a three-dimensional structural model of the target area based on the results of the sub-layer division; and performs reservoir lithology identification and lithofacies division, and discretizes the lithofacies curve to establish a lithofacies model under the control of the three-dimensional structural model of the target area; and establishes an initial three-dimensional geological attribute model of the work area based on the logging curves and lithofacies model of the horizontal wells;

[0073] A discretization module, which inputs the logging data of the horizontal wells into the initial three-dimensional geological attribute model of the work area, and converts the logging data of the horizontal wells into lithofacies codes corresponding to the reservoir and non-reservoir. The discretized data and the lithofacies data of the surrounding wells participate in the lithofacies modeling operation to form an iteratively updated lithofacies model;

[0074] A modeling module, wherein the modeling module discretizes the logging curve of the current horizontal well, and performs phase-controlled three-dimensional attribute modeling with the logging curve of the surrounding control wells under the control of the lithofacies model to form an iteratively updated three-dimensional geological model;

[0075] An analysis module, wherein the analysis module cuts a three-dimensional well logging attribute model slice corresponding to the drilling curve along the current horizontal well trajectory, analyzes the spatial position of the horizontal well trajectory in the reservoir, and provides well deviation data required for geosteering decision-making;

[0076] The geosteering module receives new logging and LWD data and implements geosteering by iteratively updating the three-dimensional model using logging and LWD data.

[0077] Example:

[0078] See also Figure 7 The present invention provides a new steering method based on a dynamic three-dimensional geological model of logging while drilling, comprising the following steps:

[0079] S1: Compare and divide the small layers according to the logging data of the control wells around the horizontal wells. The present invention takes the shale oil reservoir of the HH40 platform of Changqing Oilfield as an example. First, draw the small layer comparison section around the platform well field, take the mudstone at the bottom of Chang 73 developed in the whole area as the marker layer, and subdivide each layer into 2 sublayers to complete the small layer comparison of all control wells. See the schematic diagram of the comparison section for details. Figure 8 .

[0080] S2: Based on the results of the sub-layer division, the 3D structural model of the target area was built using geological modeling software. The reservoir of the surrounding control well logging interpretation was converted into a sandstone phase code of 1, and the non-reservoir segment was assigned a mudstone phase code of 0. The lithofacies code was discretized, and the sequential indicator interpolation algorithm was used under the constraints of the structural model. The major range was set to 5000, the minor range was set to 4000, and the azimuth was set to 30° to interpolate and generate the lithofacies model. The well logging attribute model was established under the premise of phase control. The porosity, oil saturation, permeability, and resistivity of the mudstone phase were assigned to 0, the shale content was assigned to 100, and the natural gamma was assigned to 150. The attribute model of the sandstone phase was established using the sequential Gaussian random simulation interpolation algorithm. The major range was set to 5000, the minor range was set to 4000, and the azimuth was set to 30°. The initial models of natural gamma, resistivity, shale content, porosity, saturation, and permeability under phase control conditions were established.

[0081] S3: Add the current depth data, trajectory data, lithology logging data, and gamma data of horizontal well HH40-3 to the model;

[0082] S4: Convert the horizontal well logging data into lithofacies codes corresponding to reservoirs and non-reservoir layers. The reservoir is set to have a sandstone facies code of 1, and the non-reservoir is set to have a mudstone facies code of 0, and establish an iterative lithofacies model.

[0083] S5: By calling the on-site gamma while drilling from the transmission database and the natural gamma from the surrounding wells, the phase-controlled natural gamma attribute model is established under the control of lithofacies.

[0084] S6: HH40-3 well, the overall formation is down-dip, the current well depth is 2654m, the well inclination is 89.4°, the while drilling gamma is 131.13API, the lithology is dark gray mudstone, corresponding Fig. 9 By calculating the angle within the range of 20m, that is, predicting the angle within the range of 2674m, this method is applied to reduce 0.1° for the first time to 89.3°. The calculated point values ​​are shown in Table 1. There are 9 points where the natural gamma change rate R is greater than 0.1, which does not meet the set conditions.

[0085] Table 1

[0086]

[0087]

[0088] The well inclination was reduced again by 0.1° to 89.2°. The calculated point values ​​are shown in Table 2. There were 9 points where the natural gamma change rate R was greater than 0.1, which did not meet the setting conditions.

[0089] Table 2

[0090]

[0091]

[0092] The third time, the well inclination was reduced by 0.1° to 89.1°. The calculated point values ​​are shown in Table 3. There are 6 points with R values ​​greater than 0.1, which do not meet the setting conditions.

[0093] Table 3

[0094]

[0095] The fourth time, the well inclination was reduced by 0.1° to 89°. The calculated point values ​​are shown in Table 4. The natural gamma change rates of all values ​​were less than 0.1, which met the setting conditions.

[0096] Table 4

[0097]

[0098] Through iterative calculation, the reasonable well inclination is 89° and feedback is given; at 2692m, the measured well inclination is: 89°, lithology: light gray oil-stained fine sandstone, GR106API while drilling. This indicates that after drilling out the interlayer, the oil layer is encountered. At this time, the section shows Fig.10 .

[0099] S7: After continuously calling the newly received logging and LWD data, steps S3 to S6 are repeated to iteratively update the 3D model using logging and LWD and intelligently determine the reasonable well deviation to complete the geological guidance of the well. Finally, after drilling is completed, the reservoir encounter rate reaches 87.6%.

[0100] A terminal device is provided in one embodiment of the present invention. The terminal device of this embodiment includes: a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps in the above-mentioned method embodiments are implemented. Alternatively, when the processor executes the computer program, the functions of the modules / units in the above-mentioned device embodiments are implemented.

[0101] The computer program may be divided into one or more modules / units, and the one or more modules / units are stored in the memory and executed by the processor to accomplish the present invention.

[0102] The terminal device may be a computing device such as a desktop computer, a notebook, a PDA, a cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0103] The processor can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.

[0104] The memory may be used to store the computer programs and / or modules, and the processor implements various functions of the terminal device by running or executing the computer programs and / or modules stored in the memory and calling the data stored in the memory.

[0105] If the module / unit integrated in the terminal device is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0106] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A steering method based on a dynamic three-dimensional geological model of logging while drilling, characterized in that: include: S1: Compare and divide the logging data of the control wells around the horizontal well into small layers; S2: Based on the results of sub-layer division, a three-dimensional structural model of the target area is established; Reservoir lithology identification and lithofacies division are also carried out. The lithofacies model is established under the control of the three-dimensional structural model of the target area by discretizing the lithofacies curve; based on the logging curves and lithofacies model of the horizontal wells, the initial three-dimensional geological attribute model of the work area is established; S3: Input the logging data of the horizontal wells into the initial three-dimensional geological attribute model of the work area, and convert the logging data of the horizontal wells into lithofacies codes corresponding to the reservoir and non-reservoir. The discretized data and the lithofacies data of the surrounding wells are used in lithofacies modeling operations to form an iteratively updated lithofacies model. S4: Discretize the logging curve of the current horizontal well, and perform phase-controlled three-dimensional attribute modeling with the logging curves of the surrounding control wells under the control of the lithofacies model to form an iteratively updated three-dimensional geological model; S5: Cut the 3D logging attribute model slice corresponding to the drilling curve along the current horizontal well trajectory, analyze the spatial position of the horizontal well trajectory in the reservoir, and provide the well deviation data required for geosteering decision-making; S6: After receiving the new logging and LWD data, repeat steps S3 to S5 to implement geosteering by iteratively updating the 3D model using the logging and LWD data.

2. The steering method based on the dynamic three-dimensional geological model of logging while drilling according to claim 1, characterized in that: The sub-layer comparison and division of the logging data of the control wells around the horizontal well is specifically as follows: the well selection range of the control wells is the wells within the range of a vertical well extrapolated from the vertical wells around the horizontal well; the sub-layer comparison is performed using the methods of equal formation thickness, sedimentary cycles and marker layers, and the well-connected profile of the sub-layer comparison must be closed.

3. The steering method based on the dynamic three-dimensional geological model of logging while drilling according to claim 2, characterized in that: Based on the results of the sub-layer division, a three-dimensional structural model of the target area is established; and reservoir lithology identification and lithofacies division are carried out, and the lithofacies model is established under the control of the three-dimensional structural model of the target area by discretizing the lithofacies curve; based on the logging curve and lithofacies model of the horizontal well, an initial three-dimensional geological attribute model of the work area is established, specifically: based on the results of the sub-layer division and the geological modeling software, a three-dimensional structural model of the target area is constructed, the logging interpretation reservoir is taken as the reservoir-like lithofacies, the non-reservoir section is taken as the mudstone lithofacies, and the lithofacies code is discretized, and under the constraint of the three-dimensional structural model of the target area, the lithofacies model is generated by interpolation through a sequential indicator interpolation algorithm; a logging attribute model is established under the premise of lithofacies control, and the porosity, oil saturation, permeability and resistivity of the mudstone phase are assigned; the attribute model of the sandstone phase adopts a sequential Gaussian random simulation interpolation algorithm to establish an initial three-dimensional geological attribute model of the work area of ​​natural gamma, resistivity, mud content, porosity, saturation and permeability under the premise of lithofacies control.

4. The steering method based on the dynamic three-dimensional geological model of logging while drilling according to claim 3 is characterized in that: The logging data of the horizontal well includes: current depth data of the horizontal well, trajectory data, lithologic logging data and gamma data while drilling; the converted horizontal well logging data is lithofacies code corresponding to the reservoir and non-reservoir, and the discretized data and the lithofacies data of the surrounding wells participate in lithofacies modeling operations to form an iteratively updated lithofacies model, specifically: constructing an iterative lithofacies model by means of pre-set interpolation algorithms and variogram parameters, automatically calling the latest logging lithologic data and the lithologic data of the surrounding wells for lithologic modeling.

5. The steering method based on the dynamic three-dimensional geological model of logging while drilling according to claim 4, characterized in that: The logging curves of the horizontal well include gamma while drilling and resistivity while drilling logging curves; before the logging curves of the horizontal well are discretized and compared with the logging curves of the surrounding control wells for phase-controlled three-dimensional attribute modeling under the control of the lithofacies model, it also includes: standardizing the gamma while drilling, resistivity while drilling logging curves and the logging curves of the surrounding control wells by using the trend surface method or the marker layer average correction method; The discretized current horizontal well logging curve is used to perform phase-controlled three-dimensional attribute modeling with the logging curves of the surrounding control wells under the control of the lithofacies model. Specifically, the latest logging curve while drilling and the logging curves of the surrounding wells are called to participate in the calculation through the interpolation algorithm and variation function parameters set in advance, so as to realize the iterative update of the three-dimensional geological model.

6. The steering method based on the dynamic three-dimensional geological model of logging while drilling according to claim 5, characterized in that: The three-dimensional logging attribute model slice corresponding to the drilling curve is cut along the current horizontal well trajectory, the spatial position of the horizontal well trajectory in the reservoir is analyzed, and the well inclination data required for geological steering decision-making is provided. Specifically, if the trajectory touches the lithology boundary upward, a decrease in inclination prompt is issued, and if it touches the lithology boundary downward, an increase in inclination prompt is issued; under the condition of the trajectory inside the reservoir, the curve data of the drill bit position point indicated by the latest trajectory and the straight line range extending 100m while maintaining the current well inclination angle are used as the judgment range, and the judgment curve is the natural gamma while drilling. The judgment curve is judged by the three-dimensional model grid value of every 1m in the extension direction and the current natural gamma, as shown in formula (1): Where R is the rate of change of natural gamma, dimensionless; GR 模型值 , the natural gamma value of the grid corresponding to the current decision point, API; GR while drilling, the current latest natural gamma value while drilling, API; The maximum well inclination angle for determining increase or decrease is set manually, with the default setting of 10°. If the change is within 10°, the adjusted well inclination angle data is automatically calculated and submitted to the geological steering decision-making link; if it exceeds 10°, a reminder is issued for manual intervention and judgment.

7. A guidance system based on a dynamic three-dimensional geological model of logging while drilling, characterized in that: include: A sub-layer comparison module, which performs sub-layer comparison and division on the logging data of the control wells around the horizontal well; A construction module, wherein the construction module establishes a three-dimensional structural model of the target area based on the results of the sub-layer division; and performs reservoir lithology identification and lithofacies division, and discretizes the lithofacies curve to establish a lithofacies model under the control of the three-dimensional structural model of the target area; and establishes an initial three-dimensional geological attribute model of the work area based on the logging curves and lithofacies model of the horizontal wells; A discretization module, which inputs the logging data of the horizontal wells into the initial three-dimensional geological attribute model of the work area, and converts the logging data of the horizontal wells into lithofacies codes corresponding to the reservoir and non-reservoir. The discretized data and the lithofacies data of the surrounding wells participate in the lithofacies modeling operation to form an iteratively updated lithofacies model; A modeling module, wherein the modeling module discretizes the logging curve of the current horizontal well, and performs phase-controlled three-dimensional attribute modeling with the logging curve of the surrounding control wells under the control of the lithofacies model to form an iteratively updated three-dimensional geological model; An analysis module, wherein the analysis module cuts a three-dimensional well logging attribute model slice corresponding to the drilling curve along the current horizontal well trajectory, analyzes the spatial position of the horizontal well trajectory in the reservoir, and provides well deviation data required for geosteering decision-making; The geosteering module receives new logging and LWD data and implements geosteering by iteratively updating the three-dimensional model using logging and LWD data.

8. A terminal device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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