Seismic steering method and device for tight gas horizontal well and computer equipment

Abstract

The invention relates to a tight gas horizontal well earthquake guiding method and device and computer equipment. The method comprises the following steps: predicting a stratigraphic dip angle of a horizontal well in a target area by utilizing data of the horizontal well in the target area and at least one of pre-stack time migration or pre-stack depth migration; according to the real-time while-drilling information of the horizontal well in the target area, a sand shale prediction model under different geological conditions is established through dynamic post-stack geostatistics inversion, and the spatial distribution range of sand shale is recognized; dynamic pre-stack geostatistical inversion is carried out by using real-time while-drilling information of the horizontal well in the target area, and the distribution range of the gas-containing sand body is predicted; and according to the stratigraphic dip angle, the spatial distribution range of the sand shale and the distribution range of the gas-containing sand body, horizontal well earthquake guiding is conducted. According to the method, the seismic dip angle, the sand shale space distribution and the gas-containing sweet spot space distribution are predicted through the tight gas horizontal well seismic steering method, horizontal well drilling is guided, and the drilling rate of a target body is increased.

Description

Technical Field

[0001] This specification relates to the field of oil and gas field exploration and development, and in particular to a seismic steering method, apparatus and computer equipment for tight gas horizontal wells. Background Technology

[0002] With the rapid advancement of unconventional oil and gas exploration and development, horizontal wells have become the most effective method for tight gas development. Scholars have conducted extensive research on horizontal well drilling tracking, and seismic data, with its high lateral resolution, can effectively predict the spatial distribution of geological bodies. However, the application of seismic data as the primary source for providing seismic guidance services for tight gas horizontal wells, integrating well logging, drilling, and other related disciplines, is still in its early stages.

[0003] The Sichuan Basin has complex geological conditions, with well-developed normal faults causing distortions in the occurrence of sand bodies beneath time-shifted faults, making it easy to miss the target. Simultaneously, channel sand bodies are vertically stacked and laterally interlocked, with extensive thin mudstone interlayers, reducing the sand body encounter rate. The reservoirs are highly heterogeneous, with significant differences in gas-bearing capacity, making it difficult to encounter gas-bearing "sweet spots." Conventional seismic exploration techniques are ineffective, reducing the reliability of formation dip angle, sand body, and gas-bearing "sweet spot" predictions, resulting in a low horizontal well target encounter rate. Summary of the Invention

[0004] To address the problems of the prior art, this specification provides an embodiment of a seismic steering method, apparatus, and computer equipment for tight gas horizontal wells.

[0005] This specification provides an embodiment of a seismic steering method for tight gas horizontal wells. The method includes: predicting the formation dip angle of the horizontal wells in the target area using horizontal well data and at least one of pre-stack time migration or pre-stack depth migration; establishing a sandstone and mudstone prediction model under different geological conditions using real-time drilling information of the horizontal wells in the target area and identifying the spatial distribution range of sandstone and mudstone using dynamic post-stack geostatistical inversion; conducting dynamic pre-stack geostatistical inversion using the real-time drilling information of the horizontal wells in the target area to predict the distribution range of gas-bearing sand bodies; and performing seismic steering of the horizontal wells based on the formation dip angle, the spatial distribution range of sandstone and mudstone, and the distribution range of gas-bearing sand bodies.

[0006] According to one aspect of the embodiments of this specification, predicting the formation dip angle of a horizontal well in a target area using at least one of pre-stack time migration or pre-stack depth migration includes: performing forward modeling analysis on the geological conditions surrounding the horizontal well in the target area to obtain forward modeling analysis results; calculating the degree of agreement between the forward modeling analysis results and the pre-stack time migration forward modeling, and the degree of agreement between the forward modeling analysis results and the pre-stack depth migration forward modeling; if the degree of agreement between the forward modeling analysis results and the pre-stack time migration forward modeling is high, selecting pre-stack time migration data from blocks near the horizontal well and inputting it into the time-depth conversion model of the target area for dynamic time-depth conversion to predict the formation dip angle of the horizontal well in the target area; if the degree of agreement between the forward modeling analysis results and the pre-stack depth migration forward modeling is high, selecting data from blocks near the horizontal well and performing dynamic anisotropic pre-stack depth migration to predict the formation dip angle of the horizontal well in the target area.

[0007] According to one aspect of the embodiments of this specification, pre-stack time migration data of blocks near horizontal wells are selected and input into a time-depth conversion model for a target area for dynamic time-depth conversion. Predicting the formation dip angle of horizontal wells in the target area includes: establishing an initial time-depth conversion velocity model for the target area using well-seismic calibration velocity in the target area; updating the initial time-depth conversion velocity model using stratigraphic interpretation data of the area near the horizontal well in the target area and drilling data of the vertical section of the horizontal well, and obtaining the dynamic time-depth conversion result output by the model; determining whether the dynamic time-depth conversion result matches the target entry point; if not, extracting dynamic stratigraphic data near the target entry point, updating the initial time-depth conversion velocity model for dynamic time-depth conversion, until the output result of the time-depth conversion matches the target entry point, and using the time-depth conversion result to predict the formation dip angle of horizontal wells in the target area.

[0008] According to one aspect of the embodiments of this specification, dynamic anisotropic pre-stack depth migration is carried out using data from a block near a horizontal well to predict the formation dip angle of the horizontal well in the target area. This includes: establishing an initial anisotropic parameter model for the target area using stratigraphic interpretation of the area near the target area, and performing initial anisotropic depth migration; updating the initial anisotropic parameter model using well logging stratification of the vertical section of the horizontal well and the well-seismic error of the initial anisotropic depth migration, and performing anisotropic depth migration using the updated anisotropic parameter model; determining whether the anisotropic imaging matches the target depth; if they match, determining whether the migrated CIP gather is flattened; if the gather is flattened, the output results are used for seismic steering in the horizontal section; if the gather is not flattened, the anisotropic parameters are updated, and the output results after flattening the gather are used to guide seismic steering in the horizontal section; if they do not match, the well logging stratification closest to the target point is selected, the anisotropic parameter model is updated, and anisotropic pre-stack depth migration is performed.

[0009] According to one aspect of the embodiments of this specification, the establishment of a sandstone and mudstone prediction model under different geological conditions using real-time drilling information of horizontal wells in the target area and dynamic post-stack geostatistical inversion includes: predicting the spatial distribution of sandstone and mudstone in the target area of ​​the sand body using post-stack geostatistical inversion based on drilling data of adjacent wells already drilled in the target area, and determining the trajectory of the horizontal well; adding the gamma-ray information of the horizontal well being drilled to the pre-constructed sandstone and mudstone distribution model, carrying out dynamic post-stack geostatistical inversion, obtaining the optimized spatial distribution range of sandstone and mudstone, and dynamically adjusting the trajectory of the horizontal well.

[0010] According to one aspect of the embodiments of this specification, using real-time drilling information from horizontal wells in the target area to perform dynamic pre-stack geostatistical inversion to predict the distribution range of gas-bearing sand bodies includes: using pre-stack geostatistical inversion based on data from adjacent drilled wells in the target area to predict the spatial distribution of gas-bearing sand bodies in the target area and determine the trajectory of horizontal wells; adding the sonic logging information from the horizontal wells being drilled to the pre-constructed gas-bearing sand body model to perform dynamic pre-stack geostatistical inversion on the target area, optimizing the distribution range of the gas-bearing sand bodies, and dynamically adjusting the trajectory of horizontal wells.

[0011] This specification also provides a seismic steering device for tight gas horizontal wells. The device includes: a formation dip prediction unit, used to predict the formation dip of horizontal wells in the target area using horizontal well data and at least one of pre-stack time migration or pre-stack depth migration; a sandstone and mudstone prediction unit, used to establish sandstone and mudstone prediction models under different geological conditions using real-time drilling information of horizontal wells in the target area and dynamic post-stack geostatistical inversion, and to identify the spatial distribution range of sandstone and mudstone; a gas-bearing sandstone prediction unit, used to perform dynamic pre-stack geostatistical inversion using real-time drilling information of horizontal wells in the target area, and to predict the distribution range of gas-bearing sandstone; and a seismic steering unit, used to perform seismic steering of the horizontal wells based on the formation dip, the spatial distribution range of sandstone and mudstone, and the distribution range of gas-bearing sandstone.

[0012] This specification also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the tight gas horizontal well seismic steering method.

[0013] This specification also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the tight gas horizontal well seismic steering method.

[0014] This manual combines the results of model forward modeling analysis with the following methods: using actual drilled horizontal well information to perform dynamic time-depth conversion, dynamic anisotropic pre-stack depth migration to predict seismic dip angle, using gamma-ray information while drilling to perform post-stack geostatistical inversion to predict the spatial distribution of sandstone and mudstone, and using sonic information while drilling to perform pre-stack geostatistical inversion to predict the spatial distribution of gas-bearing "sweet spots". The acquired seismic information guides horizontal well drilling, effectively improving the drilling rate of sandstone bodies and gas-bearing "sweet spots". Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments or prior art of this specification, the drawings used in the description of the embodiments or prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this specification. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 The diagram shown is a flowchart of a seismic steering method for a tight gas horizontal well according to an embodiment of this specification.

[0017] Figure 2 The diagram shown is a flowchart of a method for predicting formation dip angle using a selected offset method, according to an embodiment of this specification.

[0018] Figure 3 The diagram shown is a flowchart of a method for predicting formation dip using pre-stack time migration data according to an embodiment of this specification.

[0019] Figure 4 The diagram shown is a flowchart of a method for predicting formation dip using pre-stack depth migration, as described in this specification.

[0020] Figure 5 The diagram shown is a flowchart of a method for establishing a prediction model for sandstone and mudstone according to an embodiment of this specification.

[0021] Figure 6 The diagram shown is a flowchart of a method for predicting the gas content of sand bodies according to an embodiment of this specification.

[0022] Figure 7 The diagram shown is a structural schematic of a tight gas horizontal well seismic steering device according to an embodiment of this specification.

[0023] Figure 8 The diagram shown is a schematic representation of an embodiment of this specification using pre-stack time migration and dynamic time-depth conversion to track horizontal wells in a seismic profile.

[0024] Figure 9 The diagram shown is a schematic diagram of an integrated dynamic anisotropic pre-stack depth migration profile horizontal well tracking according to an embodiment of this specification.

[0025] Figure 10 The diagram shown is a schematic representation of a horizontal well tracking method using dynamic post-stack geostatistical lithology inversion overlay profiles, as described in this specification.

[0026] Figure 11 The diagram shown is a structural schematic of a computer device according to an embodiment of this specification.

[0027] Explanation of symbols in the attached drawings:

[0028] 701. Stratigraphic dip prediction unit;

[0029] 702, Sandstone and Mudstone Prediction Unit;

[0030] 703. Prediction Unit for Gas-Bearing Sand Bodies;

[0031] 704. Seismic steering unit;

[0032] 1102. Computer equipment;

[0033] 1104. Processor;

[0034] 1106. Memory;

[0035] 1108. Drive mechanism;

[0036] 1110. Input / output module;

[0037] 1112. Input devices;

[0038] 1114. Output devices;

[0039] 1116. Presentation device;

[0040] 1118. Graphical User Interface;

[0041] 1120. Network interface;

[0042] 1122. Communication link;

[0043] 1124. Communication bus. Detailed Implementation

[0044] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this specification, and not all embodiments. Based on the embodiments in this specification, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this specification.

[0045] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, apparatus, product, or device that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or devices.

[0046] This specification provides the operational steps of the methods described in the embodiments or flowcharts, but based on conventional or non-inventive labor, more or fewer operational steps may be included. The order of steps listed in the embodiments is merely one possible execution order among many and does not represent the only possible execution order. In actual system or device products, the methods shown in the embodiments or drawings can be executed sequentially or in parallel.

[0047] It should be noted that the seismic steering method for tight gas horizontal wells described in this specification can be used in the field of oil and gas field exploration and development, as well as in the field of geophysical technology. This specification does not limit the application field of the seismic steering method for tight gas horizontal wells.

[0048] Figure 1 The diagram shown is a flowchart of a seismic steering method for a tight gas horizontal well according to an embodiment of this specification. The method includes:

[0049] Step 101: Using horizontal well data of the target area and at least one of pre-stack time migration or pre-stack depth migration, predict the formation dip angle of the horizontal wells in the target area.

[0050] In this step, the horizontal well data includes: geological data, seismic data interpretation, drilling data, and well logging data. The geological data includes basic information such as the geological structure, stratigraphic sedimentary characteristics, and lithological variations of the horizontal wells in the target area. Seismic data interpretation involves using seismic data for stratigraphic tracing and structural interpretation, identifying structural features such as reflection interfaces and faults in the subsurface strata. Drilling and well logging data include: drill core samples, well logging curves, and data on the lithology, physical properties, and hydrocarbon content of the subsurface strata.

[0051] Based on data from horizontal wells in the target area, a well-constrained model was constructed for forward modeling analysis. Different migration methods (including but not limited to pre-stack time migration or pre-stack depth migration) were used to obtain the seismic response of sand bodies under different geological conditions. This specification selects a superior migration method to predict formation attitude, thereby guiding the seismic steering of horizontal wells.

[0052] In the embodiments of this specification, a pre-stack data volume is obtained by acquiring the data set without stacking processing (i.e., dynamic and static correction stacking) during seismic data processing. The pre-stack data volume contains differential information that varies with parameters such as offset and azimuth, which is of great significance for understanding the anisotropic characteristics and fluid characteristics of subsurface rock strata. In seismic data processing, pre-stack data volumes are commonly used for advanced imaging techniques such as pre-stack time migration or pre-stack depth migration. Pre-stack time migration (PSTM) is a technique to improve imaging accuracy in seismic data processing. It can adapt to situations with large variations in longitudinal and lateral velocities and is suitable for migration imaging at large dip angles. Pre-stack depth migration (PSDM) utilizes the propagation path and velocity model of seismic waves to reposition reflected waves from seismic records to their true subsurface depth locations, accurately reflecting the morphology and location of complex subsurface structures.

[0053] In this specification, the dip angle represents the maximum angle between the bedding plane of a rock stratum and the horizontal plane, reflecting the degree of inclination of the stratum. Besides being an important part of stratum attitude, predicting the dip angle is crucial in geology. It not only helps in understanding the spatial distribution and morphological characteristics of strata but also forms the basis for studying geological structures, sedimentary history, and mineral resource distribution. The study of stratum attitude and dip angle can provide a scientific basis for geological exploration, mineral resource development, and geological disaster prevention.

[0054] Step 102: Using real-time drilling information from horizontal wells in the target area, a prediction model for sandstone and mudstone under different geological conditions is established using dynamic post-stack geostatistical inversion to identify the spatial distribution range of sandstone and mudstone.

[0055] In this step, sandstone and mudstone models under different geological conditions are established using horizontal well data from the target area. Forward modeling is then performed, and different inversion methods are used to identify sandstone and mudstone in the forward-modeled seismic data. The optimal inversion method is selected to predict sand bodies for horizontal well seismic guidance. Forward modeling analysis shows that post-stack geostatistical inversion meets the prediction accuracy requirements for sand bodies within the work area, making it the preferred method for predicting sand bodies. Using gamma ray information from the horizontal wells being drilled, post-stack dynamic geostatistical inversion is used to predict the spatial distribution range of sand and mudstone, guiding the horizontal well drilling.

[0056] Step 103: Using the real-time drilling information of the horizontal wells in the target area, perform dynamic pre-stack geostatistical inversion to predict the distribution range of gas-bearing sand bodies.

[0057] This step involves an integrated "well logging-seismic-geological" analysis of high-quality reservoirs within the target area to determine the physical parameters of gas-sensitive rocks. Based on the geological conditions of the target area, the seismic responses of sand bodies and gas-bearing sand bodies are modeled using well-constrained forward modeling. Different inversion methods are then employed to identify gas-bearing sand bodies from the forward-modeled seismic data. Forward modeling analysis shows that pre-stack geostatistical inversion meets the required prediction accuracy for gas-bearing sand bodies within the area; therefore, pre-stack geostatistical inversion is the preferred method for predicting gas-bearing sand bodies. Dynamic pre-stack geostatistical inversion is then conducted using sonic logging information while drilling to predict the distribution range of gas-bearing sand bodies and guide horizontal well drilling.

[0058] Step 104: Based on the dip angle of the formation, the spatial distribution range of sandstone and mudstone and the distribution range of gas-bearing sand bodies, perform horizontal well seismic guidance.

[0059] In this step, for different geological conditions, and in conjunction with the model forward modeling results, dynamic time-depth conversion is performed using actual drilled horizontal well information; seismic dip angle is predicted using dynamic anisotropic pre-stack depth migration; post-stack geostatistical inversion is performed using drilling gamma information to predict the spatial distribution of sandstone and mudstone; and pre-stack geostatistical inversion is performed using drilling sonic information to predict the spatial distribution of gas-bearing "sweet spots." Specifically, the seismic dip angle is derived from pre-stack time or depth migration data; the spatial distribution of sandstone and mudstone is derived from lithological seismic data volumes in post-stack geostatistical inversion; and the spatial distribution of "sweet spots" refers to gas-bearing sand bodies, which are Vp / VS seismic data volumes representing gas-bearing characteristics derived from pre-stack geostatistical inversion.

[0060] Based on the above dynamic geological attributes, the predicted dynamic seismic information was used to guide horizontal well drilling, which effectively improved the drilling rate of sand bodies and gas-bearing "sweet spots".

[0061] Figure 2 The diagram shown is a flowchart of a method for predicting formation dip angle using a selected migration method according to an embodiment of this specification, which specifically includes the following steps:

[0062] Step 201: Perform forward modeling analysis on the geological conditions surrounding the horizontal wells in the target area to obtain the forward modeling results. In this step, an initial geological model is established using well logging data and seismic data from the horizontal wells in the target area. The parameters and structure of the initial geological model are adjusted and optimized using the high-frequency, high-resolution information from the well logging data and the mid-frequency, wide-area coverage characteristics of the seismic data, making the forward modeling results of the initial geological model closer to the actual seismic record data, until certain convergence conditions are met or the preset accuracy requirements are achieved.

[0063] Step 202: Calculate the degree of agreement between the forward modeling results and the pre-stack time migration forward modeling, and the degree of agreement between the forward modeling results and the pre-stack depth migration forward modeling. In this step, the degree of agreement can be determined by calculating the ratio of the number of points in the forward modeling results that have the same value as the pre-stack time migration forward modeling over a certain period to the total number of points in that period; similarly, the degree of agreement can be determined by calculating the ratio of the number of points in the forward modeling results that have the same value as the pre-stack depth migration forward modeling over a certain depth distance or depth segment to the total number of points in that depth segment.

[0064] Step 203: If the forward modeling results are in high agreement with the pre-stack time migration forward modeling, select the pre-stack time migration data of the block near the horizontal well and input it into the target area time-depth conversion model to perform dynamic time-depth conversion and predict the formation dip angle of the horizontal well in the target area.

[0065] In this step, if the forward modeling results show a high degree of agreement with the pre-stack time migration forward modeling, it indicates that the pre-stack time migration and forward modeling results are closer, and using pre-stack time migration to predict formation dip angles is more accurate. Therefore, a time-depth conversion model for the target area is constructed, and the pre-stack time migration data is input into this model to predict the formation dip angle of horizontal wells.

[0066] Step 204: If the forward modeling results are in high agreement with the pre-stack depth migration forward modeling, select data from the block near the horizontal well to carry out dynamic anisotropic pre-stack depth migration and predict the formation dip angle of the horizontal well in the target area.

[0067] In this step, if the forward modeling results show a high degree of agreement with the pre-stack depth migration forward modeling, it indicates that the pre-stack depth migration and forward modeling results are closer, and using pre-stack depth migration to predict formation dip angles is more accurate. Therefore, based on geological and seismic data, a target area anisotropic model is constructed that fully considers the anisotropic characteristics of the subsurface medium. The pre-stack depth migration data is then input into the target area anisotropic model to predict the formation dip angle of horizontal wells.

[0068] Velocity model establishment: Establish an initial velocity model. Using specific algorithms and methods, estimate the dynamic anisotropy parameters of the subsurface medium, such as anisotropy coefficients and orientation.

[0069] Pre-stack depth migration processing: Based on dynamic anisotropic parameters, a pre-stack depth migration algorithm is used to process seismic data. This process requires repeated iterations and optimization of the velocity model to obtain the best imaging results. The imaging results after pre-stack depth migration processing are interpreted and analyzed to extract subsurface geological structure information.

[0070] Figure 3The diagram shown is a flowchart of a method for predicting formation dip using pre-stack time migration data according to an embodiment of this specification, which specifically includes the following steps:

[0071] Step 301: Using the well-seismic calibration velocity of the target area, establish an initial model of the time-depth conversion velocity of the target area.

[0072] In this step, a comprehensive seismic geological study is conducted on the target area, and an initial model of the time-depth conversion velocity of the target area is established using well-seismic calibration velocities.

[0073] Specifically, the process begins by acquiring drilling, logging, and seismic data for the target area. Velocity information (e.g., velocity spectrum) from the seismic data is then used to analyze formation velocities. Next, the drilling and logging data are used to establish a correlation between surface time and depth. Finally, synthetic seismic records, VSP (vertical seismic profile) data, or other well-seismic calibration methods are employed to compare the drilling, logging, and seismic data for the target area. Based on the surface time-depth relationship, the parameters and locations of the synthetic records are adjusted to achieve a precise match between the seismic and geological data.

[0074] Specifically, velocity information from different layers in well logging data and velocity information from seismic data are used for velocity modeling and interpolation to construct an initial time-depth converted velocity model covering the entire target area. The output data of the initial time-depth converted velocity model for the target area is depth data. The initial time-depth converted velocity model transforms seismic data from the time domain to the depth domain and establishes the correspondence between time and depth based on velocity data, so as to more accurately interpret geological structures.

[0075] Step 302: Using the stratigraphic interpretation data of the area near the horizontal well in the target area and the drilling data of the vertical section of the horizontal well, update the initial model of the time-depth conversion rate to obtain the dynamic time-depth conversion result output by the model.

[0076] In this specification, the design of a horizontal well typically includes a vertical section, an directional ramp section, and a horizontal section. Stratification interpretation refers to the high-precision division and description of subsurface geological strata through detailed analysis and interpretation of geological data, geophysical data (such as seismic data), and drilling and logging data. The drilling data for the vertical section of a horizontal well, also known as stratification dynamic data, refers to the relevant data collected during the ongoing horizontal drilling operation. This drilling data can be acquired in real-time during the drilling process and is known data. It records key information such as strata division, depth, geological characteristics, and reservoir properties along the horizontal well trajectory.

[0077] In this step, the initial time-depth conversion velocity model is adjusted and optimized using known geological information or drilling data from the area near the horizontal well, along with the output of the initial model. Furthermore, the model is updated using known layered dynamic data from the vertical section of the horizontal well, performing dynamic time-depth conversion. This transforms the seismic data from the time domain to the depth domain.

[0078] Step 303: Determine whether the dynamic time-depth conversion result matches the target entry.

[0079] In the embodiments of this specification, "target entry" can be understood as the predetermined geological design target starting point. In horizontal well target entry, during drilling operations, once the wellbore trajectory reaches the target point, precise control of the drill bit and drill string movement allows the wellbore trajectory to smoothly enter the oil and gas formation. Therefore, when the dynamic time-depth conversion result output by the time-depth conversion rate model matches the target point, it indicates that the drilling of the horizontal section of the horizontal well can be guided by the output result of the time-depth conversion model, enabling precise wellbore trajectory control.

[0080] Step 304: If the results do not match, extract the dynamic data of the layer near the target entry point, update the initial model of the time-depth conversion velocity, and perform dynamic time-depth conversion until the time-depth conversion result matches the target entry point. Then, use the time-depth conversion result to predict the formation dip angle of the horizontal well in the target area.

[0081] If the dynamic time-depth conversion result output by the time-depth conversion velocity model does not match the target point, it indicates that the output of the time-depth conversion model is still not accurate enough. Therefore, layered dynamic data from the area near the target point are extracted, and the time-depth conversion velocity model is iteratively updated until the model's output time-depth conversion result matches the target point. At this point, the time-depth conversion velocity model is considered complete, and its output can be used to guide the trajectory tracking of horizontal wells. For example, if the horizontal section of a well is 1000 meters long, and 300 meters have been drilled, with the remaining 700 meters to be drilled, the pre-stack time migration data corresponding to the 300-meter drilled horizontal section can be input into the constructed time-depth conversion velocity model to obtain a predicted formation dip angle for the 700-meter horizontal section.

[0082] In the embodiments described in this specification, the time-depth conversion velocity model can be continuously updated and iterated using pre-stack time migration data of the drilled horizontal segments during the drilling process, thereby obtaining a more accurate prediction of the dip angle of the un-drilled horizontal segments. For example, the pre-stack time migration data corresponding to the drilled 300-meter horizontal segment can be input into the constructed time-depth conversion velocity model to obtain a prediction of the dip angle of the 300-meter to 310-meter horizontal segment. The pre-stack data of the first 310 meters can then be used to optimize the time-depth conversion velocity model in real time, further guiding the drilling process from the 310-meter to the 320-meter segment. This specification does not limit the specific location of the drilling operation to be used with the time-depth conversion velocity model.

[0083] Figure 4 The diagram shown is a flowchart of a method for predicting formation dip using pre-stack depth migration, as described in this specification.

[0084] Step 401: Using the stratigraphic interpretation of the area near the target area, establish an initial model of the anisotropic parameters of the target area and perform initial anisotropic pre-stack depth migration.

[0085] In oil exploration, subsurface rocks often exhibit anisotropic characteristics, affecting the propagation and reflection properties of seismic waves, thus impacting the accuracy of seismic data interpretation. In this step, based on the anisotropic pre-stack depth migration of the target area, and according to the stratigraphic interpretation of the surrounding area, local fine-grained synthetic record calibration is performed. An initial anisotropic parameter model is constructed, yielding the anisotropic depth migration of the pre-stack data volume output by the initial anisotropic parameter model. The initial anisotropic parameter model is a physical model that considers the differences in the physical properties of the subsurface medium (such as velocity and density) in various directions.

[0086] Step 402: Using the logging stratification of the vertical section of the horizontal well and the well-seismic error of the initial anisotropic pre-stack depth migration, update the initial model of the anisotropic parameters, and use the updated anisotropic parameter model to perform anisotropic depth migration.

[0087] In this specification, the logging stratification of the vertical section of a horizontal well is dynamic, recorded as drilling data. This data records key information such as formation division, depth, geological characteristics, and reservoir properties along the vertical well trajectory. In this step, the logging stratification data of the vertical section is extracted, compared and calculated with the anisotropic depth offset output by the initial anisotropic parameter model to obtain the wellbore vibration error. The initial anisotropic parameter model is then iteratively updated to obtain the updated model output anisotropic parameter V. p0 , δ, ε, and perform anisotropic depth migration.

[0088] Step 403: Determine whether the anisotropic imaging matches the target depth.

[0089] Step 404: If the match is found, determine whether the offset gather has been flattened. If the gather has been flattened, output the anisotropy parameter results for horizontal segment seismic steering. If the gather has not been flattened, update the anisotropy parameters and use the output results after flattening the gather to guide horizontal segment seismic steering.

[0090] Step 405: If there is a mismatch, select the logging layer closest to the target point, update the anisotropic parameter model, and perform anisotropic pre-stack depth migration. In this step, if the anisotropic imaging does not match the target depth, select the logging layer closest to the target point, continue iteratively updating the anisotropic parameter model, obtain the parameters output by the updated model, and perform migration imaging to eliminate well-seismic errors at the target point.

[0091] Figure 5 The diagram shown is a flowchart of a method for establishing a prediction model for sandstone and mudstone according to an embodiment of this specification, which specifically includes the following steps:

[0092] Step 501: Based on the drilling data of adjacent wells already drilled in the target area, use post-stack geostatistical inversion to predict the spatial distribution of sand and mudstone in the target area of ​​the sand body and determine the trajectory of the horizontal well.

[0093] In this step, existing stratigraphic information and drilling data from adjacent drilled wells are used to perform regional well-seismic integrated geostatistical inversion, conduct comprehensive seismic-geological research on the region, characterize the spatial distribution patterns of sandstone and mudstone, and design horizontal well trajectories. For example... Figure 10 As shown.

[0094] Step 502: Use the gamma-ray information from the drilling horizontal well to add it to the pre-constructed sandstone and mudstone distribution model, carry out dynamic post-stack geostatistical inversion, obtain the optimized spatial distribution range of sandstone and mudstone, and dynamically adjust the horizontal well trajectory.

[0095] In this step, during the actual drilling of the horizontal section of the horizontal well, the gamma ray information from the drilling process or the gamma ray information while drilling is input into a pre-constructed sandstone and mudstone distribution model. Dynamic geostatistical inversion is then performed on the sandstone and mudstone distribution model to obtain the lithological property body and the optimized spatial distribution range of sand and mud. Real-time seismic steering is then applied to the horizontal section to dynamically adjust the horizontal well trajectory. The gamma ray information while drilling is acquired using a gamma ray measurement system (GR) instrument. Gamma ray information while drilling refers to the formation's natural gamma ray value and related data acquired in real time during oil drilling through a gamma ray measurement system. This information helps to accurately analyze downhole geological conditions, optimize drilling design and construction technology in a timely manner, and improve drilling success rate and oil and gas recovery rate.

[0096] In the field of oil and gas field exploration and development, the rocks in the formation contain different types of radioactive elements, which release gamma rays during their decay process. The logging-while-drilling (GR) instrument measures the intensity of these gamma rays to reflect geological characteristics such as lithology and clay content of the formation. GR is a type of logging-while-drilling (LWD) technology that uses a gamma ray detector connected to the drill string to measure the intensity of gamma rays emitted during the nuclear decay of naturally occurring radioactive elements in the formation in real time during drilling. Therefore, the gamma ray information obtained during drilling can reflect changes in formation lithology.

[0097] In this step, after training the initial model for predicting the distribution of sandstone and mudstone, a prediction model for the distribution of sandstone and mudstone is obtained. Furthermore, based on the predictions of the prediction model for the distribution of sandstone and mudstone, the drilling of the horizontal well is tracked in real time.

[0098] Figure 6The diagram shown is a flowchart of a method for predicting the gas content of sand bodies according to an embodiment of this specification.

[0099] Step 601: Based on data from adjacent drilled wells in the target area, pre-stack geostatistical inversion is used to predict the spatial distribution of gas-bearing sand bodies in the target area and determine the horizontal well trajectory. In this step, the pre-stack geostatistical inversion meets the prediction accuracy requirements for gas-bearing sand bodies in the area. Utilizing existing stratigraphic information and drilling data from adjacent drilled wells, the pre-stack geostatistical inversion is optimized to predict gas-bearing sand bodies, and the horizontal well trajectory is designed.

[0100] In this step, based on high-precision acquired data, key technologies such as the "six-part method" for low-frequency protection of AVO characteristics, high-fidelity pre-stack denoising, near-surface Q compensation, and OVT domain pre-stack time migration are employed to obtain high-precision gathers that meet the requirements of simultaneous pre-stack inversion. Using existing drilling, logging, and well logging data from the target area, rock physics sensitive parameter analysis is conducted to determine gas-bearing sensitive rock physics parameters. Regional integrated well-seismic pre-stack inversion, combined with other relevant studies, is used to predict the distribution range of gas-rich "sweet spots" and design horizontal well trajectories.

[0101] Step 602: Use the sonic logging information from the horizontal well during drilling to add it to the pre-constructed gas-bearing sand body model, perform dynamic pre-stack geostatistical inversion on the target area, optimize the distribution range of the gas-bearing sand body, and dynamically adjust the trajectory of the horizontal well.

[0102] Specifically, using the sonic logging information from the horizontal well being drilled, dynamic pre-stack geostatistical inversion is carried out to predict the distribution range of gas-bearing sand bodies and guide the drilling of the horizontal well.

[0103] Gas-bearing differences near the horizontal well platform were analyzed. Gather optimization was performed on selected local areas, utilizing newly drilled pilot wells and adjacent wells of the horizontal well in the target area until an ideal pre-stack gather was obtained. Further, using information from newly drilled pilot wells or adjacent wells of the horizontal well in the target area, combined with the optimized gather, a local integrated well-seismic pre-stack inversion was performed to optimize the predicted results of the target area inversion, which represents the distribution range of the gas-bearing favorable zone. The new prediction results were then used to guide the horizontal section of the horizontal well during drilling.

[0104] After adopting the technology described in this manual, the drilling rate of sandstone reservoirs increased from an average of about 70% to an average of about 90%, and the drilling rate of gas-bearing "sweet spots" increased from an average of 60%-70% to an average of 80%. The drilling cycle for a single well decreased to 3-5 days, and production costs were significantly reduced. Combined with horizontal well technology modification, in the horizontal wells that have been tested in the Jinqiu Gas Field, the average daily production capacity at the wellhead increased by 90,000 cubic meters, and production efficiency was significantly improved.

[0105] This manual, based on dynamic seismic exploration theory, utilizes seismic data, geological information, drilling data, well logging data, and well logging information to address different geological conditions. It combines model forward modeling results with information from actual horizontal well drilling to perform dynamic time-depth conversion, process anisotropic pre-stack depth migration to predict seismic dip angles, use post-stack geostatistical inversion to predict the spatial distribution of sandstone and mudstone using gamma-ray information while drilling, and use pre-stack geostatistical inversion to predict the spatial distribution of gas-bearing "sweet spots" using sonic information while drilling. The acquired seismic information guides horizontal well drilling, effectively improving the encounter rate of sandstone bodies and gas-bearing "sweet spots."

[0106] like Figure 7 The diagram shown is a structural schematic of a tight gas horizontal well seismic steering device according to an embodiment of this specification. The basic structure of the device is illustrated in this diagram. The functional units and modules can be implemented using software, or using general-purpose chips or specific chips to implement tight gas horizontal well seismic steering. The device specifically includes:

[0107] Formation dip angle prediction unit 701 is used to predict the formation dip angle of horizontal wells in the target area using horizontal well data of the target area and at least one of pre-stack time migration or pre-stack depth migration.

[0108] The sandstone and mudstone prediction unit 702 is used to utilize real-time drilling information from horizontal wells in the target area to conduct dynamic post-stack geostatistical inversion to establish sandstone and mudstone prediction models under different geological conditions and identify the spatial distribution range of sandstone and mudstone.

[0109] The gas-bearing sand body prediction unit 703 is used to perform dynamic pre-stack geostatistical inversion using real-time drilling information from horizontal wells in the target area to predict the distribution range of gas-bearing sand bodies.

[0110] The seismic guidance unit 704 is used to guide the horizontal well seismically according to the dip angle of the strata, the spatial distribution range of sandstone and mudstone and the distribution range of gas-bearing sand bodies.

[0111] Figure 8 The diagram illustrates an embodiment of this specification using pre-stack time migration and dynamic time-depth conversion to track horizontal wells along seismic profiles. The pre-stack time migration is input into the time-depth conversion velocity model to obtain the predicted formation dip angle, which guides the drilling process of the horizontal well.

[0112] Figure 9The diagram illustrates a horizontal well tracking method using integrated dynamic anisotropic pre-stack depth migration profiles, as described in this specification. During horizontal well geological design, based on regional anisotropic pre-stack depth migration, a small block near the horizontal well platform is selected for detailed stratigraphic interpretation. This involves local fine-grained synthetic record calibration, anisotropic modeling, and anisotropic depth migration, providing more accurate data for later use of dynamic pre-stack depth migration and reducing the number of iterations.

[0113] Figure 10 The diagram shown is a schematic representation of a horizontal well tracking method using dynamic post-stack geostatistical lithology inversion overlay profiles, as described in this specification. It utilizes existing stratigraphic information and drilling data to perform integrated well-seismic geostatistical inversion of the region, enabling comprehensive seismic-geological studies and characterizing the region as follows: Figure 10 Based on the spatial distribution pattern of sand and mudstone shown, horizontal well trajectory design is carried out.

[0114] like Figure 11 The illustration shows a computer device provided in an embodiment of this specification. The tight gas horizontal well seismic steering method described in this application can be applied to the computer device. The computer device 1102 may include one or more processors 1104, such as one or more central processing units (CPUs), each of which can implement one or more hardware threads. The computer device 1102 may also include any memory 1106 for storing information of any kind, such as code, settings, data, etc. Non-limitingly, for example, the memory 1106 may include any type of RAM, any type of ROM, flash memory device, hard disk, optical disk, etc. More generally, any memory can use any technology to store information. Further, any memory can provide volatile or non-volatile retention of information. Further, any memory can represent a fixed or removable component of the computer device 1102. In one case, when the processor 1104 executes associated instructions stored in any memory or combination of memories, the computer device 1102 can perform any operation of the associated instructions. The computer device 1102 also includes one or more drive mechanisms 1108 for interacting with any memory, such as a hard disk drive mechanism, an optical disk drive mechanism, etc.

[0115] Computer device 1102 may also include an input / output module 1110 (I / O) for receiving various inputs (via input device 1112) and providing various outputs (via output device 1114). A specific output mechanism may include a presentation device 1116 and an associated graphical user interface (GUI) 1118. In other embodiments, the input / output module 1110 (I / O), input device 1112, and output device 1114 may be omitted, and the device may function solely as a computer device within a network. Computer device 1102 may also include one or more network interfaces 1120 for exchanging data with other devices via one or more communication links 1122. One or more communication buses 1124 couple the components described above together.

[0116] Communication link 1122 can be implemented in any way, such as via a local area network, a wide area network (e.g., the Internet), a point-to-point connection, or any combination thereof. Communication link 1122 may include any combination of hardwired links, wireless links, routers, gateway functions, name servers, etc., governed by any protocol or combination of protocols.

[0117] Corresponding to Figures 1 to 6 In addition to the methods described above, embodiments of this specification also provide a computer-readable storage medium storing a computer program that, when executed by a processor, performs the steps of the methods described above.

[0118] This specification also provides computer-readable instructions, wherein when a processor executes the instructions, the program therein causes the processor to perform the following... Figures 1 to 6 The method shown.

[0119] It should be understood that in the various embodiments of this specification, the sequence number of each process does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this specification.

[0120] It should also be understood that, in the embodiments of this specification, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this specification generally indicates that the preceding and following related objects have an "or" relationship.

[0121] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed in this specification can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of each example have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this specification.

[0122] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0123] In the several embodiments provided in this specification, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the couplings or direct couplings or communication connections shown or discussed may be indirect couplings or communication connections through some interfaces, devices, or units, or they may be electrical, mechanical, or other forms of connection.

[0124] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of the embodiments described in this specification, depending on actual needs.

[0125] Furthermore, the functional units in the various embodiments of this specification can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0126] If the integrated unit is implemented as 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 technical solution of this specification, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this specification. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0127] This specification uses specific embodiments to illustrate the principles and implementation methods of this specification. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this specification. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this specification. Therefore, the content of this specification should not be construed as a limitation of this specification.

Claims

1. A seismic steering method for tight gas horizontal wells, characterized in that, The method includes: Using horizontal well data of the target area and at least one of pre-stack time migration or pre-stack depth migration, predict the formation dip angle of horizontal wells in the target area; Using real-time drilling information from horizontal wells in the target area, dynamic post-stack geostatistical inversion is employed to establish sandstone and mudstone prediction models under different geological conditions, and to identify the spatial distribution range of sandstone and mudstone. Using real-time drilling information from horizontal wells in the target area, dynamic pre-stack geostatistical inversion is performed to predict the distribution range of gas-bearing sand bodies; Based on the dip angle of the strata, the spatial distribution range of sandstone and mudstone, and the distribution range of gas-bearing sand bodies, horizontal well seismic guidance is carried out.

2. The method according to claim 1, characterized in that, Predicting the dip angle of horizontal wells in a target area using at least one of pre-stack time migration or pre-stack depth migration includes: A forward modeling analysis was performed on the geological conditions surrounding the horizontal well in the target area to obtain the forward modeling analysis results; Calculate the degree of agreement between the forward modeling results and the pre-stack time migration forward modeling results, and the degree of agreement between the forward modeling results and the pre-stack depth migration forward modeling results, respectively. If the forward modeling results are in high agreement with the pre-stack time migration forward modeling, the pre-stack time migration data of the block near the horizontal well is selected and input into the time-depth conversion model of the target area for dynamic time-depth conversion to predict the formation dip angle of the horizontal well in the target area. If the forward modeling results are in high agreement with the pre-stack depth migration forward modeling, data from blocks near horizontal wells can be selected to conduct dynamic anisotropic pre-stack depth migration to predict the dip angle of horizontal wells in the target area.

3. The method according to claim 1, characterized in that, Prestack time migration data from blocks near horizontal wells are selected and input into the target area time-depth conversion model for dynamic time-depth conversion. The predicted formation dip angle of horizontal wells in the target area includes: Using the well-seismic calibration velocity in the target area, an initial model of the time-depth conversion velocity in the target area is established; Using the stratigraphic interpretation data of the area near the horizontal well in the target region and the drilling data of the vertical section of the horizontal well, the initial model of the time-depth conversion rate is updated to obtain the dynamic time-depth conversion result output by the model. Determine whether the dynamic time-depth conversion result matches the target entry result; If they do not match, extract the dynamic data of the layers near the target point, update the initial model of the time-depth conversion velocity, and perform dynamic time-depth conversion until the time-depth conversion result matches the target. Then, use the time-depth conversion result to predict the formation dip angle of the horizontal well in the target area.

4. The method according to claim 2, characterized in that, Dynamic anisotropic pre-stack depth migration was performed using data from blocks near horizontal wells to predict the formation dip angles of horizontal wells in the target area, including: Using the stratigraphic interpretation of the area near the target region, an initial model of anisotropic parameters for the target region is established, and initial anisotropic pre-stack depth migration is performed. Using the logging stratification of the vertical section of the horizontal well and the well-seismic error of the initial anisotropic pre-stack depth migration, the initial model of the anisotropic parameters is updated, and the updated anisotropic parameter model is used to perform anisotropic depth migration. Determine whether the anisotropic imaging matches the target depth; If they match, determine whether the shifted gather has been flattened; If the gather is flattened, the output results are used for horizontal segment seismic guidance. If the gather is not flattened, update the anisotropy parameters and use the output results after flattening the gather to guide the seismic orientation of the horizontal segment. If they do not match, select the logging layer closest to the target point, update the anisotropic parameter model, and perform anisotropic pre-stack depth migration.

5. The method according to claim 1, characterized in that, Using real-time drilling information from horizontal wells in the target area, dynamic post-stack geostatistical inversion was employed to establish prediction models for sandstone and mudstone under different geological conditions, including: Based on data from adjacent drilled wells in the target area, post-stack geostatistical inversion is used to predict the spatial distribution of sand and mudstone in the target area of ​​the sand body and determine the trajectory of horizontal wells. The gamma-ray information from the drilling of the horizontal well is incorporated into a pre-constructed sandstone and mudstone distribution model. Dynamic post-stack geostatistical inversion is then performed to obtain the optimized spatial distribution range of sandstone and mudstone, and the trajectory of the horizontal well is dynamically adjusted.

6. The method according to claim 1, characterized in that, Using real-time drilling information from horizontal wells in the target area, dynamic pre-stack geostatistical inversion is performed to predict the distribution range of gas-bearing sand bodies, including: Based on data from adjacent drilled wells in the target area, pre-stack geostatistical inversion is used to predict the spatial distribution of gas-bearing sand bodies in the target area and determine the trajectory of horizontal wells. The sonic logging information from the horizontal well during drilling is incorporated into a pre-constructed gas-bearing sand body model to perform dynamic pre-stack geostatistical inversion of the target area, optimize the distribution range of the gas-bearing sand body, and dynamically adjust the trajectory of the horizontal well.

7. A seismic steering device for a tight gas horizontal well, characterized in that, The device includes: The formation dip angle prediction unit is used to predict the formation dip angle of horizontal wells in the target area using horizontal well data of the target area and at least one of pre-stack time migration or pre-stack depth migration. The sandstone and mudstone prediction unit is used to establish sandstone and mudstone prediction models under different geological conditions by using real-time drilling information from horizontal wells in the target area and dynamic post-stack geostatistical inversion, and to identify the spatial distribution range of sandstone and mudstone. The gas-bearing sand body prediction unit is used to perform dynamic pre-stack geostatistical inversion using real-time drilling information from horizontal wells in the target area to predict the distribution range of gas-bearing sand bodies. The seismic steering unit is used to guide horizontal wells seismically based on the dip angle of the formation, the spatial distribution range of sandstone and mudstone, and the distribution range of gas-bearing sand bodies.

8. A computer 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, it implements the method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the method according to any one of claims 1 to 6.

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