Well-seismic combination reservoir space distribution identification method, device, equipment and medium

Abstract

This invention provides a method, apparatus, equipment, and medium for identifying reservoir spatial distribution using a combination of well logging and seismic analysis. Through the establishment of a well logging lithofacies and sedimentary facies identification module, the rock type and sedimentary microfacies of the target strata in the study area are quickly and accurately identified. Based on this, the planar distribution of sedimentary microfacies is characterized. Combined with well point information from drilled wells, the dominant sedimentary microfacies and their vertical and horizontal development patterns for effective reservoir development in the target strata are derived. Furthermore, by organically combining facies-controlled 3D geological modeling, isochronous stratigraphic slicing, and 90° phase transformation techniques, a reservoir spatial distribution prediction model is established. This model reduces the ambiguity of geophysical data and the inaccuracy of local structural predictions, achieving reservoir spatial distribution prediction accuracy at an advanced level both domestically and internationally. Simultaneously, the planar distribution characteristics of single-stage sand bodies are used as one of the model constraints to improve the identification ability and accuracy of vertical reservoir variation patterns, solving the problem that traditional seismic and geological disciplines only focus on planar reservoir distribution prediction.

Description

Technical Field

[0001] This invention relates to the field of oil and gas field exploration and development, and more specifically, to a method for identifying reservoir spatial distribution using a combination of well and seismic testing, a device for identifying reservoir spatial distribution using a combination of well and seismic testing, and equipment and computer-readable storage medium for implementing the method for identifying reservoir spatial distribution using a combination of well and seismic testing. Background Technology

[0002] The Sulige Gas Field is a typical example of tight sandstone gas reservoirs in China, currently ranking first in China and among the top ten in the world in terms of annual natural gas production. The main target layers of the Upper Paleozoic gas reservoir are the Heba Member and the Shanyi Member, deposited in a braided river sedimentary environment under a gentle tectonic setting. The river channels migrate rapidly, resulting in poor reservoir continuity and extremely high heterogeneity. With the deepening of exploration and development, the gas field is currently showing a significant decline in resource quality. The secondary and tertiary reservoir areas surrounding the enriched areas have poor reservoir properties, thin thickness, and unclear longitudinal and lateral variation patterns, hindering drilling success rates and reservoir encounter rates. Therefore, improving the accuracy and precision of reservoir spatial distribution prediction is of great significance for the efficient development and stable production of the gas field.

[0003] Currently, research on reservoir spatial distribution prediction mainly focuses on hydrocarbon reservoirs such as carbonate rocks and shale. These reservoirs typically have thick individual reservoir layers, relatively stable sedimentation, and minimal vertical and horizontal variations, making them easier to predict. However, these existing research results are not ideal when applied to predicting reservoirs with strong heterogeneity and small thickness and scale. This is because traditional seismic and geological techniques are relatively focused on predicting planar reservoir distribution, often failing to accurately reflect the vertical variations of the reservoir. Furthermore, their prediction accuracy is insufficient to guide horizontal well operations, significantly limiting the improvement of horizontal well reservoir encounter rates. Therefore, existing methods and technologies are not suitable for guiding reservoir prediction in fluvial, highly heterogeneous, tight sandstone gas reservoirs, such as those in the Sulige gas field. Summary of the Invention

[0004] The purpose of this invention is to address at least one of the aforementioned shortcomings of the prior art. For example, one objective of this invention is to provide a method for identifying reservoir spatial distribution by combining well-seismic analysis, which overcomes the problems of multiple interpretations of geophysical data and inaccurate prediction of local structures, thereby improving the ability and accuracy of identifying reservoir variation patterns vertically, and ultimately increasing the drilling success rate of vertical cluster wells and the reservoir encounter rate of horizontal wells.

[0005] To achieve the above objectives, the present invention provides a method for identifying reservoir spatial distribution by combining well and seismic analysis.

[0006] The method for identifying reservoir spatial distribution by combining well and seismic analysis includes the following steps:

[0007] S1. Based on the core sampling, cuttings, and logging data of the target block, confirm the stratigraphic layer data, structural prediction map of the sub-layers, planar distribution map of sedimentary microfacies, sand body thickness prediction map, and reservoir thickness prediction map, confirm the range of well logging curve values ​​of reservoirs with good sandstone physical property sensitivity in the target block, and confirm the effective vertical and horizontal development patterns of reservoirs in the well area in combination with test and production data.

[0008] S2. Based on the seismic data of the target block, the multi-stage sand body planar distribution prediction results of the target layer are obtained. Combined with the well point data of the actual drilled well, the planar distribution characteristics of the single-stage sand body between wells with the highest degree of consistency with the target single-stage sand body distribution are confirmed.

[0009] S3. Based on the stratigraphic layering data, the structural prediction map of the sub-layers, the planar distribution map of sedimentary microfacies, the sand body thickness prediction map and the reservoir thickness prediction map, the planar distribution characteristics of single-stage sand bodies between wells, the range of reservoir logging curve values, and the longitudinal and transverse development law of the effective reservoir in the well area, establish a reservoir spatial distribution prediction model to predict the reservoir spatial distribution.

[0010] In an exemplary embodiment of the well-seismic combined method for identifying reservoir spatial distribution of the present invention, the reservoir of the target block may be a strongly heterogeneous tight sandstone reservoir.

[0011] In an exemplary embodiment of the well-seismic combined method for identifying reservoir spatial distribution of the present invention, step S1 may further include:

[0012] S11. Obtain core and cuttings data of the target block to classify rock types and sedimentary microfacies, confirm the main developed rock types and sedimentary microfacies types of the target block, and establish a well logging lithofacies and sedimentary facies identification module.

[0013] S12. The lithology and sedimentary microfacies of the target strata that have been drilled in the target block are identified by the well logging lithology and sedimentary microfacies identification module. Based on the identification results, the strata are finely divided into multiple sub-layers to obtain strata layering data. In addition, the sedimentary microfacies planar distribution map, sand body thickness prediction map and reservoir thickness prediction map of the multiple sub-layers are obtained by combining seismic data.

[0014] S13. A high-precision velocity model is established by performing fine calibration of the synthetic record based on the stratigraphic layering data. The layer structure of the multiple sub-layers is recovered based on the high-precision velocity model. The micro-structure of the well area to be deployed is confirmed based on the maximum curvature attribute of the earthquake and the stratigraphic layering data, and the structural prediction map of each sub-layer is obtained.

[0015] S14. Conduct a correlation analysis between reservoir properties and logging curves for the target block, identify the logging curve type that is more sensitive to sandstone properties in the target block, and determine the range of reservoir logging curve values ​​corresponding to the logging curve type.

[0016] S15. Evaluate the reservoir based on test and production data, determine the dominant sedimentary microfacies and structural characteristics of effective reservoir development, and confirm the longitudinal and lateral development patterns of the effective reservoir in the target block.

[0017] In an exemplary embodiment of the well-seismic combined identification method for reservoir spatial distribution of the present invention, in step S13, the formation layering data used in the fine calibration of the synthetic record may include the layering data of horizontal wells.

[0018] In an exemplary embodiment of the well-seismic combined method for identifying reservoir spatial distribution of the present invention, in step S14, the type of well logging curve with the best sensitivity to sandstone properties in the target block can be a gamma curve, and the sandstone can be divided into high-quality reservoirs and reservoirs according to the range of gamma curve values.

[0019] In an exemplary embodiment of the well-seismic combined method for identifying reservoir spatial distribution of the present invention, the range of gamma curve values ​​can be determined according to the target layer. The reservoir gamma values ​​in Box 7 and Box 8 are 50 to 70 API, and the gamma values ​​of high-quality reservoirs are <50 API; the reservoir gamma values ​​in Shan 1 are 60 to 80 API, and the gamma values ​​of high-quality reservoirs are <60 API.

[0020] In an exemplary embodiment of the well-seismic combined method for identifying reservoir spatial distribution according to the present invention, step S2 may include:

[0021] S21. Use isochronous stratigraphic slices or 90° phase transformation to obtain the multi-stage sand body planar distribution prediction results of the target stratum.

[0022] S22. Based on the multi-stage sand body planar distribution prediction results of the target stratum and the actual drilling well point data, compare and verify to select the single-stage sand body planar distribution prediction results that have the highest degree of conformity with the target single-stage sand body distribution, and confirm the planar distribution characteristics of the inter-well single-stage sand body based on the single-stage sand body planar distribution prediction results.

[0023] In an exemplary embodiment of the well-seismic combined method for identifying reservoir spatial distribution of the present invention, step S3 may include:

[0024] S31. Establish a three-dimensional structural model based on the stratigraphic layer data and the structural prediction map of the sub-layer. Establish a reservoir spatial distribution prediction model based on the three-dimensional structural model, the planar distribution map of sedimentary microfacies of the sub-layer, the sand body thickness prediction map and the reservoir thickness prediction map, the planar distribution characteristics of single-stage sand bodies between wells, the range of reservoir logging curve values, and the longitudinal and transverse development law of the effective reservoir in the well area. Use the reservoir spatial distribution prediction model to predict the reservoir spatial distribution.

[0025] In an exemplary embodiment of the well-seismic combined method for identifying reservoir spatial distribution of the present invention, the reservoir spatial distribution prediction model can be modified according to the effective longitudinal and transverse development law of the reservoir and the control effect of micro-structures on gas and water.

[0026] In another aspect, the present invention provides a well-seismic combined method for identifying reservoir spatial distribution. The well-seismic combined method for identifying reservoir spatial distribution includes, in sequence, a geological structure and reservoir characteristic confirmation module, a single-stage sand body planar distribution characteristic confirmation module, and a reservoir spatial distribution prediction module.

[0027] The geological structure and reservoir characteristic confirmation module is configured to confirm stratigraphic layering data, structural prediction maps of sub-layers, planar distribution maps of sedimentary microfacies, sand body thickness prediction maps, and reservoir thickness prediction maps based on core and cuttings data of the target block; confirm the range of well logging curve values ​​of reservoirs with good sandstone physical property sensitivity within the target block; and confirm the effective vertical and horizontal development patterns of reservoirs in the well area by combining test and production data.

[0028] The single-stage sand body planar distribution feature confirmation module is configured to obtain the multi-stage sand body planar distribution prediction results of the target layer based on the seismic data of the target block, and combine the actual drilling well point data to confirm the planar distribution features of the inter-well single-stage sand body with the highest degree of consistency with the target single-stage sand body distribution.

[0029] The reservoir space distribution prediction module is configured to establish a three-dimensional structural model based on the stratigraphic layer data and the structural prediction map of the sub-layers; establish a reservoir space distribution prediction model based on the three-dimensional structural model, the planar distribution map of sedimentary microfacies of the sub-layers, the sand body thickness prediction map and the reservoir thickness prediction map, the planar distribution characteristics of single-stage sand bodies between wells, the range of reservoir logging curve values, and the longitudinal and lateral development law of the effective reservoir in the well area; and predict the reservoir space distribution based on the reservoir space distribution prediction model.

[0030] In another aspect, the present invention provides a computer device, the computer device comprising:

[0031] Processor; memory storing a computer program that, when executed by the processor, implements the well-seismic combined method for identifying reservoir spatial distribution as described above.

[0032] In another aspect, the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the well-seismic combined method for identifying reservoir spatial distribution as described above.

[0033] Compared with existing technologies, the beneficial effects of this invention include: improving the ability and accuracy of identifying reservoir variation patterns vertically, and solving the problem that traditional seismic and geological disciplines only focus on the prediction of planar reservoir distribution. Using the logging-lithofacies-sedimentary facies identification module established by this invention, rock types and sedimentary microfacies can be quickly and accurately identified in the target strata of the study area. Based on this, the planar distribution of sedimentary microfacies is characterized, and combined with well point information from drilled wells, the dominant sedimentary microfacies and their vertical and horizontal development patterns for effective reservoir development in the target strata are summarized. Furthermore, through the organic combination of facies-controlled 3D geological modeling, isochronous stratigraphic slicing, 90° phase transformation, and other techniques, under the guidance of model and geological understanding, the problems of multiple interpretations of geophysical data and inaccurate local structural predictions are reduced, enabling the accuracy of reservoir spatial distribution prediction to reach advanced levels both domestically and internationally. This significantly improves the proportion of static Class I+II wells in vertical wells and the reservoir encounter rate in horizontal wells. Furthermore, the reservoir spatial distribution prediction model established in this invention, based on traditional stratigraphic data, structural prediction maps, sand body thickness prediction maps, reservoir thickness prediction maps, sedimentary microfacies plane distribution maps of sub-layers, and reservoir logging curve value ranges, also adds the plane distribution characteristics of single-stage sand bodies between wells as a constraint condition, which can further improve the prediction accuracy. Attached Figure Description

[0034] The above and other objects and / or features of the present invention will become clearer from the following description taken in conjunction with the accompanying drawings, in which:

[0035] Figure 1 A flowchart illustrating an exemplary embodiment of the well-seismic combined method for identifying reservoir spatial distribution according to the present invention is shown.

[0036] Figure 2 The diagram shows an isochronous formation slice of a horizontal well area, illustrating an exemplary embodiment of the well-seismic combined identification method for reservoir spatial distribution according to the present invention.

[0037] Figure 3 This diagram illustrates an exemplary embodiment of the well-seismic combined method for identifying reservoir spatial distribution according to the present invention, showing the combined well-seismic reconstruction of formation structure and actual drilling trajectory.

[0038] Figure 4 A schematic diagram of the actual drilling trajectory of a horizontal well and a sandstone / mudstone prediction model is shown as an exemplary embodiment of the well-seismic combined method for identifying reservoir spatial distribution according to the present invention. Detailed Implementation

[0039] In the following sections, the well-seismic combined identification method, apparatus, device, and medium of the present invention will be described in detail with reference to exemplary embodiments.

[0040] It should be noted that the terms "first," "second," "third," etc., and "S1," "S2," "S3," etc., used in this invention are for ease of description and distinction only, and should not be construed as indicating or implying relative importance or describing a specific order or sequence. Terms such as "upper," "lower," "front," "rear," "left," "right," "inner," and "outer" are only for ease of description and establishing relative orientations or positional relationships, and do not indicate or imply that the referred component must have that specific orientation or position. Unless otherwise stated, "a plurality of" means two or more.

[0041] To address the shortcomings of traditional reservoir prediction methods described in the background section, such as their inability to identify vertical reservoir variation patterns, insufficient accuracy in predicting spatial reservoir variation patterns, and the ambiguity and inaccurate micro-structural predictions in seismic data for some well areas, the inventors proposed a well-seismic combined method for identifying reservoir spatial distribution. This method identifies the spatial distribution characteristics of tight sandstone reservoirs by cross-validating prediction results from single-well sedimentary microfacies with seismic isochronous stratigraphic slices, 90° facies rotation, and facies-controlled 3D geological models. This method comprehensively utilizes geological, seismic, logging, and modeling techniques, and is based on actual drilling and logging data. It largely overcomes the problems of ambiguity in geophysical data and inaccurate local structural predictions, thereby improving the ability and accuracy of identifying vertical reservoir variation patterns and ultimately increasing the drilling success rate of vertical cluster wells and the reservoir encounter rate of horizontal wells.

[0042] To achieve the above objectives, the present invention provides a method for identifying reservoir spatial distribution by combining well and seismic analysis.

[0043] In a first exemplary embodiment of the well-seismic combined method for identifying reservoir spatial distribution according to the present invention, the method includes the following steps:

[0044] S1. Based on the core sampling, cuttings and logging data of the target block, confirm the stratigraphic layer data, structural prediction map of the sub-layers, planar distribution map of sedimentary microfacies, sand body thickness prediction map and reservoir thickness prediction map, confirm the range of well logging curve values ​​of reservoirs with good sandstone physical property sensitivity in the target block, and confirm the effective vertical and horizontal development law of the well area in combination with test and production data.

[0045] S11. Obtain core and cuttings data of the target block to classify rock types and sedimentary microfacies, confirm the main developed rock types and sedimentary microfacies types of the target block, and establish a well logging lithofacies and sedimentary facies identification module.

[0046] S12. The lithology and sedimentary microfacies of the target strata in the target block are identified by the well logging lithofacies and sedimentary microfacies identification module. Based on the identification results, the strata are finely divided into multiple sub-layers to obtain strata layering data. In addition, the sedimentary microfacies planar distribution map, sand body thickness prediction map and reservoir thickness prediction map of multiple sub-layers are obtained by combining seismic data.

[0047] S13. A high-precision velocity model is established by performing fine calibration of the synthetic record based on the stratigraphic layering data. The layer structure of multiple small layers is recovered based on the high-precision velocity model. The micro-structure of the well area to be deployed is confirmed based on the maximum curvature attribute of the earthquake and the stratigraphic layering data, and the structural prediction map of each small layer is obtained.

[0048] Optionally, the stratigraphic data used in the fine calibration of the synthetic record may include stratigraphic data from horizontal wells.

[0049] S14. Conduct a correlation analysis between reservoir properties and logging curves in the target block to identify the logging curve types that are more sensitive to sandstone properties in the target block, and determine the range of reservoir logging curve values ​​corresponding to the logging curve types.

[0050] Optionally, the logging curve type that best reflects the physical properties of sandstone within the target block can be the gamma curve, and the sandstone can be classified into high-quality reservoirs and reservoirs based on the range of gamma curve values.

[0051] Optionally, the range of gamma curve values ​​can be determined according to the target stratum. The gamma value of the reservoir in Box 7 and Box 8 is 50-70 API, and the gamma value of high-quality reservoirs is <50 API; the gamma value of the reservoir in Shan 1 is 60-80 API, and the gamma value of high-quality reservoirs is <60 API.

[0052] S15. Evaluate the reservoir by combining test and production data, determine the dominant sedimentary microfacies and structural characteristics of effective reservoir development, and confirm the longitudinal and lateral development patterns of effective reservoirs in the target block.

[0053] S2. Based on the seismic data of the target block, the multi-stage sand body planar distribution prediction results of the target layer are obtained. Combined with the well point data of the actual drilled well, the planar distribution characteristics of the single-stage sand body between wells with the highest degree of consistency with the target single-stage sand body distribution are confirmed.

[0054] S21. Use isochronous stratigraphic slices or 90° phase transformation to obtain multi-stage sand body planar distribution prediction results for the target stratum.

[0055] S22. Based on the multi-stage sand body planar distribution prediction results of the target layer and the actual well point data, compare and verify to select the single-stage sand body planar distribution prediction results that have the highest degree of conformity with the target single-stage sand body distribution, and confirm the planar distribution characteristics of the single-stage sand body between wells based on the single-stage sand body planar distribution prediction results.

[0056] S3. Based on stratigraphic data, structural prediction maps of sub-layers, planar distribution maps of sedimentary microfacies, sand body thickness prediction maps and reservoir thickness prediction maps, planar distribution characteristics of single-stage sand bodies between wells, range of reservoir logging curve values, and longitudinal and transverse development patterns of effective reservoirs in the well area, establish a reservoir spatial distribution prediction model to predict the reservoir spatial distribution.

[0057] S31. Establish a three-dimensional structural model based on stratigraphic data and structural prediction maps of sub-layers. Based on the three-dimensional structural model, the planar distribution map of sedimentary microfacies of sub-layers, the predicted map of sand body thickness and reservoir thickness, the planar distribution characteristics of single-stage sand bodies between wells, the range of reservoir logging curve values, and the longitudinal and transverse development law of effective reservoirs in the well area, establish a reservoir spatial distribution prediction model. Use the reservoir spatial distribution prediction model to predict the reservoir spatial distribution.

[0058] Optionally, the reservoir spatial distribution prediction model can be modified based on the effective longitudinal and transverse development patterns of the reservoir and the control effect of microstructures on gas and water.

[0059] In another aspect, the present invention provides a second exemplary embodiment of a well-seismic combined method for identifying reservoir spatial distribution. The well-seismic combined method for identifying reservoir spatial distribution includes a geological structure and reservoir characteristic confirmation module, a single-phase sand body planar distribution characteristic confirmation module, and a reservoir spatial distribution prediction module, connected in sequence.

[0060] The geological structure and reservoir characteristic confirmation module is configured to confirm stratigraphic layering data, structural prediction maps of sub-layers, planar distribution maps of sedimentary microfacies, sand body thickness prediction maps, and reservoir thickness prediction maps based on core and cuttings data of the target block; confirm the range of well logging curve values ​​of reservoirs with good sandstone physical property sensitivity within the target block; and confirm the effective vertical and horizontal development patterns of reservoirs in the well area by combining test and production data.

[0061] The single-stage sand body planar distribution feature confirmation module is configured to obtain the multi-stage sand body planar distribution prediction results of the target layer based on the seismic data of the target block, and combine the actual drilling well point data to confirm the planar distribution features of the inter-well single-stage sand body with the highest degree of consistency with the target single-stage sand body distribution.

[0062] The reservoir space distribution prediction module is configured to establish a three-dimensional structural model based on stratigraphic data, structural prediction maps of sub-layers, and sedimentary microfacies plane distribution maps of sub-layers, sand body thickness prediction maps, reservoir thickness prediction maps, plane distribution characteristics of single-stage sand bodies between wells, reservoir logging curve value range, and effective longitudinal and transverse development patterns of reservoirs in the well area. The module then predicts the reservoir space distribution based on the reservoir space distribution prediction model.

[0063] In another aspect, the present invention provides a third exemplary embodiment of a computer device. The computer device includes a processor and a memory. The memory stores a computer program. The computer program is executed by the processor, causing the processor to perform at least one of the methods for identifying reservoir spatial distribution using a well-seismic combination as described in the first exemplary embodiment.

[0064] In another aspect, the present invention provides a fourth exemplary embodiment of a computer-readable storage medium storing a computer program. The computer-readable storage medium stores a computer program that, when executed by a processor, causes the processor to perform a well-seismic combined method for identifying reservoir spatial distribution as described in the first exemplary embodiment. The computer-readable recording medium is any data storage device capable of storing data readable by a computer system. Examples of computer-readable recording media include: read-only memory, random access memory, read-only optical disk, magnetic tape, floppy disk, optical data storage device, and carrier waves (such as data transmission via the Internet through wired or wireless transmission paths).

[0065] To better understand the exemplary embodiments of the present invention described above, further explanation is provided below with reference to specific embodiments and accompanying drawings. However, it is worth noting that this method is not limited to these embodiments, and the examples given are not intended to limit the invention. In the detailed description of the method below, some specific original details are elaborated. For parts not described in detail, such as the well logging-lithofacies-sedimentary facies identification module, high-precision velocity model, isochronous stratigraphic slicing technology, 90° phase conversion technology, three-dimensional geological model, and attribute model, these are all common technical means and methods in the industry, and can be fully understood by those skilled in the art; therefore, they are not explained in detail. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0066] Example 1

[0067] In this embodiment, as Figure 1 As shown, the method of identifying reservoir spatial distribution by combining well and seismic analysis can be achieved through the following steps:

[0068] Step 1: By collecting core and cuttings data from the study area, and combining this with the regional sedimentary environment and stratigraphic characteristics, the rock types and sedimentary microfacies of each sublayer in the target layers (He7, ​​He8, and Shan1) are classified. This identifies the main rock types developed in the study area, which can be used to further establish a well logging-lithofamination-sedimentary facies identification module. Specifically, He8 includes: light gray and grayish-white fine sandstone, medium sandstone, gravelly coarse sandstone, argillaceous siltstone, gray and dark gray mudstone, and silty mudstone. Shan1 includes: gray and light gray fine sandstone, grayish-white coarse sandstone, gravelly coarse sandstone, dark gray and grayish-black mudstone, carbonaceous mudstone, and coal. Sedimentary microfacies types include: midline bars, channel stagnant deposits, side bars, breach channels, natural dikes, and interchannels (floodplains, interdistributary depressions), etc.

[0069] Step 2: Establish a well logging-lithofacies-sedimentary facies identification module. This module can confirm the characteristics of well logging curves based on the rock type and sedimentary facies features of this study area. Using this well logging-lithofacies-sedimentary facies identification module, rock type and sedimentary microfacies type are identified for all drilled wells in the study area, including He7, He8, and Shan1 sections. Based on this, fine stratigraphic division is carried out to obtain stratigraphic strata. Stratigraphic strata are the stratigraphic data obtained after fine stratigraphic division. Under the joint constraints of sedimentary microfacies (sedimentary microfacies type) and marker lithology (rock type), the He7 to Shan1 sections are divided into 8 sub-layers with an average apparent thickness of about 20m. Combined with seismic data, a plane prediction map of sedimentary microfacies, sand bodies, and reservoir thickness of each sub-layer is obtained. The plane prediction map of sedimentary microfacies, sand bodies, and reservoir thickness is a prediction map drawn based on the sand body and reservoir thickness of each sub-layer after stratigraphic division.

[0070] Step 3: Using the stratigraphic data obtained in Step 2, perform fine calibration of the synthetic record, fit the time-depth curves of multiple wells, establish a high-precision velocity model, and reconstruct the main target strata in the study area based on the high-precision velocity model. At the same time, by combining the maximum curvature attribute of the seismic data with the geological stratification of the actual drilled wells, complete the micro-structure prediction of the well area to be deployed. This can be used for three-dimensional modeling in the subsequent Step 7 to maximize the accurate prediction of the micro-structure of the well area to be deployed.

[0071] Step 4: For multi-stage channel sand bodies and reservoirs or thin sand bodies that develop vertically within a single small layer, multiple planar prediction maps of sand bodies and reservoirs are obtained by using isochronous stratigraphic slicing or 90° phase transformation technology, which can achieve the ability to identify target sand bodies or reservoirs vertically to a certain extent.

[0072] By comparing the aforementioned seismic data results with the sand body and reservoir development of actual drilled wells, the planar prediction maps of sand bodies and reservoirs with the highest degree of conformity to the distribution range of single-stage sand bodies and reservoirs can be selected. The seismic characteristics of well points that do not conform to the data are analyzed and corrected. Using these maps, the single-stage channel sand bodies and reservoirs in the area to be deployed can be characterized, and the distribution characteristics of single-stage sand bodies can be confirmed, thereby improving the prediction accuracy of multiple vertical reservoirs or thin reservoirs within a small layer.

[0073] Step 5: Conduct correlation analysis between reservoir properties and logging curves to identify the logging curve types that are more sensitive to sandstone properties in the area, and determine the range of logging curve values ​​corresponding to the reservoir.

[0074] Analysis of logging curves from hundreds of wells in the target reservoir sections revealed that gamma curves have the strongest correlation with reservoir properties; lower gamma values ​​generally indicate better reservoir physical properties. In the study area, gamma values ​​for the reservoirs are mainly concentrated between 40 and 80 API. Stratified by reservoir type, the gamma values ​​for the He7 and He8 sections are <70 API, while those for high-quality reservoirs are <50 API; the gamma value for the Shan1 section is <80 API, while those for high-quality reservoirs are <60 API.

[0075] Step 6: Evaluate the reservoir based on test and production data. Compare gas and water production data from single and combined tests, correct the effective reservoir development intervals interpreted from well logging, and ultimately determine that the dominant sedimentary microfacies for effective reservoir development with industrial gas production capacity are mainly: mid-shoals, side-shoals, and channel stagnant deposits. That is, the effective reservoirs in Box 7 and the upper part of Box 8 in the central area, and the lower part of Box 8 in the western area, are all controlled to some extent by micro-structures and local traps. Based on the comprehensive sedimentary and structural characteristics of the well areas to be deployed, summarize the longitudinal and lateral development patterns of the effective reservoirs in the target intervals.

[0076] Step 7: Based on the structural features of the study area obtained from the structural restoration and micro-structural prediction in Step 4 above, establish a high-precision three-dimensional structural model. Based on the high-precision three-dimensional structural model, and combined with the results and understanding of the thickness maps of each small layer of sand bodies, sedimentary microfacies planar maps, and single-stage sand body distribution characteristics, establish a facies-controlled three-dimensional geological model under the joint constraints of well and seismic data using multi-point geostatistics methods, so as to predict the sedimentary microfacies and spatial distribution of sand bodies in the well area to be deployed.

[0077] Step 8: Using the sedimentary microfacies and sand body models from Step 7 (i.e., the spatial distribution of sedimentary microfacies and sand bodies predicted by the facies-controlled 3D geological model) as constraints, establish the corresponding gamma attribute model using the well logging curves from the drilled wells (mainly gamma curves). Set the discrimination conditions as follows: gamma values ​​of reservoirs in Box 7 and Box 8 are 50-70 API, and gamma values ​​of high-quality reservoirs are 0-50 API; gamma values ​​of reservoirs in Mountain 1 are 60-80 API, and gamma values ​​of high-quality reservoirs are 0-60 API, thus obtaining the reservoir spatial distribution prediction model.

[0078] Finally, the reservoir spatial distribution prediction model can be modified by combining the effective longitudinal and lateral development patterns of the well area and the control effect of micro-structures on gas and water, so as to obtain the final reservoir spatial distribution prediction results.

[0079] Example 2

[0080] In this embodiment, taking a horizontal well in a certain block of a certain field as an example, the reservoir space distribution of the target layer of the horizontal well trajectory location was predicted, and trajectory design and actual drilling tracking adjustment analysis were carried out accordingly. Figures 2-4 These are schematic diagrams of isochronous stratigraphic slices in a horizontal well area, schematic diagrams of well-seismic combined reconstruction of stratigraphic structure and actual well trajectory, and schematic diagrams of the actual well trajectory and sandstone / mudstone prediction model. Among them, Figure 2 The colored filling in the middle represents the trough, and the black and white filling represents the peak. The blue line on the left side of the well column is the gamma curve, and the red line on the right side is the porosity curve. The three red lines on the horizontal well trajectory position are the positions corresponding to the isochronous slices. The three images below are the planar images corresponding to the isochronous slices. The corresponding positions on the profile are marked with arrows in the images. From left to right, they are top, middle and bottom. The lines in the planar images are the actual drilled well trajectory. Figure 3 The red line represents the horizontal well trajectory, the black contour lines and values ​​represent elevation data, and the bedding plane represents the upper boundary structure of a well area predicted by this technical procedure. Red indicates high structure, and blue indicates low structure. Figure 4 The vertical axis represents elevation, and the horizontal axis represents profile, with the unit of measurement being meters; the colored bars represent the actual drilling trajectory, the left curve represents gamma ray GR in API, and the right curve represents gas logging TC in %; the black horizontal line represents the top boundary of the formation, the interlayer background filling represents the lithological prediction model, red represents high-quality reservoirs, orange represents reservoirs, yellow represents sand bodies, and white represents mudstone.

[0081] First, using the logging-lithology-sedimentary facies identification module established in the aforementioned steps, rock types and sedimentary microfacies are identified for all drilled wells in the horizontal well area.

[0082] By comparing the stratigraphy of adjacent wells, the stratigraphy of the well area is re-divided. The thickness of the sand bodies and the planar distribution of sedimentary microfacies in each sub-layer of the well area are characterized by geological stratification, and corresponding result maps are drawn.

[0083] By using geological stratification to perform synthetic record fine calibration, a high-precision velocity model was established. Combined with the maximum curvature attribute of the earthquake, the micro-structural prediction of the well area to be deployed was completed, and the structural characteristics of the horizontal well trajectory location were obtained, with the first part slightly updip at 0.4° and the latter part basically horizontal.

[0084] By analyzing the variation patterns of sand bodies and reservoirs in adjacent wells, as well as the testing and production status of adjacent wells, and combining the seismic isochronous stratigraphic slice results, it was found that the effective reservoirs in the first half of the horizontal well trajectory are mainly located in the middle and lower part of the He83 section, while the second half is mainly located in the middle and lower part of the He84 section.

[0085] Based on the above understanding and prediction results, a phase-controlled three-dimensional geological model constrained by well and seismic data was established using multi-point geostatistics. Under the control of this model, the corresponding gamma attribute model was further established using the gamma curves of the drilled wells, resulting in a reservoir space distribution prediction model.

[0086] With the help of the reservoir prediction model, a horizontal well trajectory was designed to achieve the highest reservoir encounter rate and a smooth wellbore trajectory: after entering the target in the middle of the He 83 section, the horizontal section was drilled downwards at an angle of 89-89.5° to the central control point. In the latter half of the horizontal section, the inclination was lowered to 84° to explore the lower and middle reservoir of He 84. After encountering the He 84 reservoir, the inclination could be increased in the tail section to expand the lateral exploitable range of the reservoir. Ultimately, this well successfully encountered two reservoirs, He 83 and He 84, according to the preset trajectory and pre-drilling plan. Gas logging showed good results, and the wellbore trajectory was smooth and largely consistent with the designed trajectory.

[0087] Furthermore, the method of this invention, applied to well site deployment and horizontal well geological steering in a certain risk cooperation area, significantly improved the reservoir encounter rate of horizontal wells, reduced the proportion of horizontal well sidetracking, and increased the proportion of static Class I+II wells in vertical cluster wells. A total of 61 wells were used in 2023. Compared with 2022, the proportion of static Class I+II wells in vertical cluster wells increased by 3.4%, the reservoir encounter rate of horizontal wells increased by 11.5%, and the sidetracking proportion decreased by 10.7%. Simultaneously, this technology effectively guided the parameter selection for fracturing stimulation schemes, laying a solid foundation for the continued high production in the risk cooperation block.

[0088] Although the present invention has been described above in conjunction with exemplary embodiments and accompanying drawings, those skilled in the art should understand that various modifications can be made to the above embodiments without departing from the spirit and scope of the claims.

Claims

1. A method for identifying reservoir spatial distribution using a combination of well and seismic analysis, characterized in that, The method includes the following steps: S1. Based on the core sampling, cuttings and logging data of the target block, confirm the stratigraphic layer data, structural prediction map of the sub-layers, planar distribution map of sedimentary microfacies, sand body thickness prediction map and reservoir thickness prediction map, confirm the range of well logging curve values ​​of reservoirs with good sandstone physical property sensitivity in the target block, and confirm the effective longitudinal and transverse development law of the well area in combination with test and production data. S2. Based on the seismic data of the target block, the multi-stage sand body planar distribution prediction results of the target layer are obtained, and the planar distribution characteristics of the single-stage sand body between wells with the highest degree of consistency with the target single-stage sand body distribution are confirmed by combining the actual drilling well point data. S3. Based on the stratigraphic layering data, the structural prediction map of the sub-layers, the planar distribution map of sedimentary microfacies, the sand body thickness prediction map and the reservoir thickness prediction map, the planar distribution characteristics of single-stage sand bodies between wells, the range of reservoir logging curve values, and the longitudinal and transverse development law of the effective reservoir in the well area, establish a reservoir spatial distribution prediction model to predict the reservoir spatial distribution.

2. The method for identifying reservoir spatial distribution by combining well and seismic analysis according to claim 1, characterized in that, The reservoir in the target block is a highly heterogeneous tight sandstone reservoir.

3. The method for identifying reservoir spatial distribution by combining well and seismic analysis according to claim 1, characterized in that, Step S1 further includes: S11. Obtain core and cuttings data of the target block to classify rock types and sedimentary microfacies, confirm the main developed rock types and sedimentary microfacies types of the target block, and establish a well logging lithofacies and sedimentary facies identification module; S12. The lithology and sedimentary microfacies of the target strata that have been drilled in the target block are identified by the well logging lithofacies and sedimentary microfacies identification module. Based on the identification results, the strata are finely divided into multiple small layers to obtain strata layering data. In addition, the sedimentary microfacies planar distribution map, sand body thickness prediction map and reservoir thickness prediction map of the multiple small layers are obtained by combining seismic data. S13. A high-precision velocity model is established by performing fine calibration of the synthetic record based on the stratigraphic layering data. The layer structure of the multiple sub-layers is recovered based on the high-precision velocity model. The micro-structure of the well area to be deployed is confirmed based on the maximum curvature attribute of the earthquake and the stratigraphic layering data, and the structural prediction map of each sub-layer is obtained. S14. Conduct a correlation analysis between reservoir properties and logging curves for the target block, identify the logging curve type that is more sensitive to sandstone properties in the target block, and determine the range of reservoir logging curve values ​​corresponding to the logging curve type. S15. Evaluate the reservoir based on test and production data, determine the dominant sedimentary microfacies and structural characteristics of effective reservoir development, and confirm the longitudinal and lateral development patterns of the effective reservoir in the target block.

4. The method for identifying reservoir spatial distribution by combining well and seismic analysis according to claim 3, characterized in that, In step S13, the formation layering data used in the fine calibration of the synthetic record includes the layering data of horizontal wells.

5. The method for identifying reservoir spatial distribution by combining well and seismic analysis according to claim 3, characterized in that, In step S14, the logging curve type with the best sensitivity to sandstone properties in the target block is the gamma curve, and the sandstone is divided into high-quality reservoirs and reservoirs according to the range of gamma curve values.

6. The method for identifying reservoir spatial distribution by combining well and seismic analysis according to claim 5, characterized in that, The range of gamma curve values ​​is determined according to the target formation. The reservoir gamma values ​​in Box 7 and Box 8 are 50 to 70 API, while the gamma values ​​of high-quality reservoirs are <50 API. The gamma value of the Shan 1 section reservoir is 60-80 API, while the gamma value of high-quality reservoirs is <60 API.

7. The method for identifying reservoir spatial distribution by combining well and seismic analysis according to claim 1, characterized in that, Step S2 includes: S21. Use isochronous stratigraphic slices or 90° phase transformation to obtain the multi-stage sand body planar distribution prediction results of the target stratum; S22. Based on the multi-stage sand body planar distribution prediction results of the target stratum and the actual drilling well point data, compare and verify to select the single-stage sand body planar distribution prediction results that have the highest degree of conformity with the target single-stage sand body distribution, and confirm the planar distribution characteristics of the inter-well single-stage sand body based on the single-stage sand body planar distribution prediction results.

8. The method for identifying reservoir spatial distribution by combining well and seismic analysis according to claim 1, characterized in that, Step S3 includes: S31. Establish a three-dimensional structural model based on the stratigraphic layer data and the structural prediction map of the sub-layer. Establish a reservoir spatial distribution prediction model based on the three-dimensional structural model, the planar distribution map of sedimentary microfacies of the sub-layer, the sand body thickness prediction map and the reservoir thickness prediction map, the planar distribution characteristics of single-stage sand bodies between wells, the range of reservoir logging curve values, and the longitudinal and transverse development law of the effective reservoir in the well area. Use the reservoir spatial distribution prediction model to predict the reservoir spatial distribution.

9. The method for identifying reservoir spatial distribution by combining well and seismic analysis according to claim 3, characterized in that, The reservoir spatial distribution prediction model is modified based on the effective reservoir longitudinal and transverse development patterns and the control effect of micro-structures on gas and water.

10. A device for identifying reservoir spatial distribution using a combination of well and seismic analysis, characterized in that, The well-seismic combined identification device for reservoir spatial distribution includes a geological structure and reservoir characteristic confirmation module, a single-phase sandbody planar distribution characteristic confirmation module, and a reservoir spatial distribution prediction module connected in sequence. The geological structure and reservoir characteristics confirmation module is configured to confirm the stratigraphic layering data, structural prediction map of the sub-layers, sedimentary microfacies plane distribution map, sand body thickness prediction map and reservoir thickness prediction map based on the core, cuttings and logging data of the target block; confirm the range of well logging curve values ​​of reservoirs with good sandstone physical property sensitivity in the target block; and confirm the effective vertical and horizontal development law of the well area in combination with test and production data. The single-stage sand body planar distribution feature confirmation module is configured to obtain the multi-stage sand body planar distribution prediction results of the target layer based on the seismic data of the target block, and combine the actual drilling well point data to confirm the planar distribution features of the inter-well single-stage sand body with the highest degree of consistency with the target single-stage sand body distribution. The reservoir space distribution prediction module is configured to establish a three-dimensional structural model based on the stratigraphic layer data and the structural prediction map of the sub-layers; establish a reservoir space distribution prediction model based on the three-dimensional structural model, the planar distribution map of sedimentary microfacies of the sub-layers, the sand body thickness prediction map and the reservoir thickness prediction map, the planar distribution characteristics of single-stage sand bodies between wells, the range of reservoir logging curve values, and the longitudinal and lateral development law of the effective reservoir in the well area; and predict the reservoir space distribution based on the reservoir space distribution prediction model.

11. A computer device, characterized in that, The computer device includes: At least one processor; and a memory storing program instructions, wherein the program instructions are configured to be executed by the at least one processor, the program instructions including instructions for performing the well-seismic combined method for identifying reservoir spatial distribution according to any one of claims 1 to 9.

12. A computer-readable storage medium having computer program instructions stored thereon, characterized in that, When the computer program instructions are executed by the processor, they implement the well-seismic combined identification method for reservoir spatial distribution as described in any one of claims 1 to 9.

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