Reservoir boundary quantitative prediction method and system based on sand body mechanism model forward modeling
By using a forward modeling method based on sand body mechanism models, combined with sedimentary microfacies and seismic response calibration, the accuracy problem of reservoir boundary prediction was solved, and the well deployment efficiency of oilfield development was improved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- CHINA NATIONAL OFFSHORE OIL (CHINA) CO LTD
- Filing Date
- 2023-07-07
- Publication Date
- 2026-06-02
Smart Images
Figure CN116840918B_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of oilfield development technology, and in particular to a method and system for quantitative prediction of reservoir boundaries based on forward modeling of sand body mechanism. Background Technology
[0002] Oil and gas reservoirs are underground rock formations where oil and gas accumulate during oil and gas exploration. Reservoir characteristics include lithology, physical properties, and hydrocarbon potential, which are also the main focus of reservoir prediction. Reservoir lithology describes the main characteristics of the reservoir's mineral composition, reflecting the reservoir's storage capacity and characteristics. Commonly used parameters include reservoir structure, distribution range, top interface morphology, and thickness. Reservoir boundary prediction falls under the scope of reservoir lithology prediction. It can be defined based on the reservoir encountered during actual drilling, using parameters such as reservoir structure and thickness, and then predicted using various technical methods.
[0003] Accurate prediction of reservoir boundaries is crucial for the successful implementation of development wells, especially horizontal wells, and is a key concern in oilfield drilling. Predicting reservoir distribution based on seismic and drilled well data using seismic attributes is a common method in oilfield development research; however, the accuracy of quantitative predictions is often not guaranteed. To achieve accurate reservoir boundary prediction, in addition to conventional attribute prediction, it is necessary to design a sand body mechanism model that conforms to the actual subsurface conditions based on the actual sand body boundary definition. Then, an accurate relationship needs to be established between forward modeling records and the calibration of actual seismic responses to obtain a more accurate reservoir boundary location, guiding the deployment and optimization of development wells. Summary of the Invention
[0004] To address the aforementioned problems, the present invention aims to provide a quantitative prediction method and system for reservoir boundaries based on forward modeling of sand body mechanisms. This method can determine accurate forward modeling parameters by combining the sedimentary microfacies type and scale of the target sand body, and obtain a relatively accurate reservoir boundary location by calibrating the forward model of the drilled well with the actual seismic response.
[0005] To achieve the above objectives, the present invention adopts the following technical solution: a quantitative prediction method for reservoir lithological boundaries based on forward modeling of sand body mechanism, comprising: determining the sand body mechanism model of typical well lines, clarifying the sedimentary microfacies types contained in the target sand body, the development scale of different sedimentary microfacies, and the possible combination relationships of sedimentary microfacies; determining the petrophysical parameters corresponding to different sedimentary microfacies types using well logging curves and petrophysical cross plots, simplifying all drilled well information into single-well sand body mechanism models, and performing seismic forward modeling analysis to obtain the calibration relationship between the actual seismic response and the forward modeled synthetic seismic record; establishing a reservoir model with multiple parameters of thickness and sedimentary microfacies combination to cover various possible reservoir development conditions of the target sand body, setting the reservoir parameter range corresponding to the reservoir boundary, and predicting the attribute threshold corresponding to the reservoir boundary based on the calibration relationship; delineating the reservoir boundary based on the attribute threshold to guide the optimization of development horizontal wells.
[0006] Furthermore, the sand body mechanism model for typical well-connected lines was determined, including:
[0007] Typical well profiles covering the target sand body area are selected from multiple directions. Based on the thickness of the sand body encountered in the drilled wells, the shape of the logging curves, and the range of values, the reservoir information encountered at the well points is extrapolated in combination with the lateral variation characteristics of the seismic response to obtain the sand body mechanism model of typical well lines.
[0008] Furthermore, the sedimentary microfacies types contained in the target sand body were identified, including:
[0009] The sedimentary microfacies corresponding to the target sand body include four types: the main channel, the channel margin, the overflow bank, and the interchannel mudstone.
[0010] Furthermore, the calibration relationship between the actual seismic response and the forward-modeled synthetic seismic record includes:
[0011] Analyze the gamma, sonic, and density curves of the target sand body depth range and its vicinity in the drilled well, and infer the type and thickness of the microfacies encountered in the well based on the value range distribution and morphological changes of the curves.
[0012] Using the top and bottom depths of the target sand body as the range, we analyze the range of petrophysical parameters corresponding to different lithologies through petrophysical cross plots.
[0013] Based on the combined analysis of well logging curves and petrophysical cross plots, the corresponding petrophysical parameters for different sedimentary microfacies types were determined.
[0014] A single-well sand body mechanism model is established, a wavelet with the same dominant frequency as the seismic data is selected, a synthetic seismic record is produced, the seismic attributes of the forward model record and the seismic attributes of the actual well bypass are calculated, the correlation between the two is analyzed, a linear or nonlinear calibration relationship is established, and then the true amplitude attributes are calculated by forward modeling the amplitude attributes of the multi-parameter reservoir model.
[0015] Furthermore, the corresponding petrophysical parameters for different sedimentary microfacies types were determined, including:
[0016] The main body and edges of the river channel are predominantly sandstone, while the overflow bank transitions from sandstone to mudstone. The mudstone in the river channel is pure mudstone. Considering the gradual change between different sedimentary microfacies, and combining the density and velocity ranges of different microfacies in the well logging curve analysis results, the petrophysical parameters of different microfacies are determined.
[0017] Furthermore, based on the calibration relationship, the attribute thresholds corresponding to the reservoir boundary are predicted, including:
[0018] Based on the sedimentary microfacies type covered by the target sand body, a synthetic seismic record is generated, and the attribute values of the synthetic seismic record are converted into the attribute values of the actual seismic data through calibration formulas.
[0019] Based on the experience gained from implementing development wells in the field, the reservoir boundary definition and corresponding reservoir parameters are clearly defined;
[0020] Based on the forward modeling results, the reservoir boundary definition and the corresponding attribute thresholds are determined.
[0021] Furthermore, a synthetic seismic record was created, including:
[0022] Based on the sedimentary microfacies types covered by the target sand body, reservoir thickness was set as a single variable. Forward models of different sedimentary microfacies types were established and synthetic seismic records were produced. The attribute values of the synthetic seismic records were converted into attribute values of the actual seismic data through calibration formulas.
[0023] By combining the sedimentary microfacies types covered by the target sand body, the development thickness of different sedimentary microfacies and the possible combination relationships of sedimentary microfacies are set as variables. A reservoir model with multiple parameters of thickness and sedimentary microfacies combination is established so that it can cover various possible reservoir development situations of the target sand body. Synthetic seismic records are also produced, and the attribute values of the synthetic seismic records are converted into the attribute values of the actual seismic data through calibration formulas.
[0024] A quantitative prediction system for reservoir lithological boundaries based on forward modeling of sand body mechanism models includes: a first processing module, which determines the sand body mechanism model of typical well lines, clarifies the sedimentary microfacies types contained in the target sand body, the development scale of different sedimentary microfacies, and the possible combination relationships of sedimentary microfacies; a second processing module, which uses well logging curves and petrophysical cross plots to determine the petrophysical parameters corresponding to different sedimentary microfacies types, simplifies all drilled well information into single-well sand body mechanism models, and performs seismic forward modeling analysis to obtain the calibration relationship between the actual seismic response and the forward modeled synthetic seismic record; a threshold prediction module, which establishes a reservoir model with multiple parameters of thickness and sedimentary microfacies combination to cover various possible reservoir development conditions of the target sand body, sets the reservoir parameter range corresponding to the reservoir boundary, and predicts the attribute threshold corresponding to the reservoir boundary based on the calibration relationship; and an optimization processing module, which delineates the reservoir boundary based on the attribute threshold and guides the optimization of development horizontal wells.
[0025] A computer-readable storage medium for storing one or more programs, characterized in that the one or more programs include instructions that, when executed by a computing device, cause the computing device to perform any of the methods described above.
[0026] A computing device includes: one or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs include instructions for performing any of the methods described above.
[0027] The present invention has the following advantages due to the adoption of the above technical solutions:
[0028] 1. This invention utilizes information such as reservoir information encountered in single well drilling, seismic profiles of typical interconnected wells in multiple directions, logging curves, and rock physics analysis to establish single-well sand body mechanism models and interconnected well sand body mechanism models, and integrates them to obtain a multi-parameter combined sand body mechanism model that considers all microfacies types, scales, and combinations of the target sand body.
[0029] 2. This invention establishes a relatively accurate calibration relationship between the forward modeling records of drilled wells and the actual well bypass, and then obtains the corresponding actual data response attribute values through multi-parameter simulation of the reservoir model, accurately predicting the attribute thresholds corresponding to the reservoir boundary. Attached Figure Description
[0030] Figure 1 This is a flowchart of the reservoir boundary quantitative prediction method based on forward modeling of sand body mechanism in this embodiment of the invention;
[0031] Figure 2 This is a sand body mechanism model diagram established based on a typical well passage trajectory profile in an embodiment of the present invention;
[0032] Figure 3 This embodiment of the invention uses forward-modeled synthetic seismic records obtained by abstracting the actual sand body encountered during well drilling into a mechanism model.
[0033] Figure 4 This is a diagram showing the relationship between forward modeling records and actual seismic response calibration in an embodiment of the present invention;
[0034] Figure 5 This is a sand body mechanism model with multiple parameters and a forward modeling synthesis record diagram in an embodiment of the present invention;
[0035] Figure 6a This is the actual seismic response calibration chart obtained from the forward modeling results of the sand body mechanism model based on the multi-parameter variables of sand body thickness in this embodiment of the invention;
[0036] Figure 6b This is the actual seismic response calibration chart obtained from the forward modeling results of the sand body mechanism model based on the multi-parameter variables of the sand-soil ratio in this embodiment of the invention;
[0037] Figure 7 This refers to the lithological boundary defined based on the attribute threshold in this embodiment of the invention. Detailed Implementation
[0038] The present invention will now be described in detail with reference to the accompanying drawings and embodiments.
[0039] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the described embodiments of the present invention are within the scope of protection of the present invention.
[0040] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.
[0041] In one embodiment of the present invention, a quantitative prediction method for reservoir boundaries based on forward modeling of sand body mechanisms is provided. In this embodiment, a shallow-water deltaic facies sand body unit from a Bohai oilfield is used as an example for further illustration. Figure 1 As shown, the method includes the following steps:
[0042] 1) Determine the sand body mechanism model of typical well lines, clarify the sedimentary microfacies types contained in the target sand body, the development scale of different sedimentary microfacies, and the possible combination relationships of sedimentary microfacies;
[0043] 2) Use well logging curves and rock physics cross plots to determine the rock physics parameters corresponding to different sedimentary microfacies types, simplify all drilled well information into single-well sand body mechanism models, and perform seismic forward modeling analysis to obtain the calibration relationship between the actual seismic response and the forward modeling synthetic seismic record;
[0044] 3) Establish a reservoir model with multiple parameters and a combination of thickness and sedimentary microfacies to cover various possible reservoir development conditions of the target sand body, set the reservoir parameter range corresponding to the reservoir boundary, and predict the attribute threshold corresponding to the reservoir boundary based on the calibration relationship.
[0045] 4) Delineate reservoir boundaries based on attribute thresholds to guide the optimization of development horizontal wells.
[0046] In step 1) above, the sand body mechanism model of a typical well-connecting line is determined. Specifically, typical well-crossing profiles covering the target sand body range are selected. Based on the thickness of the sand body encountered by the drilled well, the shape of the logging curve, and the range of value distribution, the reservoir information encountered at the well point is extrapolated in combination with the lateral variation characteristics of the seismic response to obtain the sand body mechanism model of the typical well-connecting line.
[0047] In step 1) above, if Figure 2 As shown, based on the sand bodies encountered during drilling and the regional sedimentary characteristics, the sedimentary microfacies types contained in the target sand body are identified. Specifically, the sedimentary microfacies corresponding to the target sand body include four types: main channel, channel margin, overflow bank, and interchannel mudstone.
[0048] In this embodiment, the thickness of a single-stage channel sand body generally does not exceed 10m, and the longitudinal direction is generally one-stage or two-stage deposition, with the stratum thickness generally not exceeding 15m and the cumulative sand body thickness generally not exceeding 15m.
[0049] In step 2) above, the calibration relationship between the actual seismic response and the forward-modeled synthetic seismic record includes the following steps:
[0050] 2.1) Analyze the gamma, sonic, and density curves of the target sand body depth range and its vicinity in the drilled well. Based on the value range distribution and morphological changes of the curves, infer the type and thickness of the microfacies encountered in the well.
[0051] In this embodiment, for example, a certain drilling well encountered two sand bodies at the target sand body depth. The upper sand body was 1.9 meters thick, and its gamma ray curve showed a funnel-shaped curve characteristic. The acoustic and density curves showed low velocity and low density characteristics and their shapes were similar to the gamma ray curve, suggesting that it was a channel margin sedimentary microfacies. The middle section encountered 4.1 meters of interchannel mudstone, and the lower section encountered a sand body with a thickness of 9.3 meters. Its gamma ray curve showed a box-shaped curve characteristic. Compared with the upper channel margin sand body curve, the acoustic and density curves showed lower velocity and lower density, suggesting that the sandstone purity was higher and it was a channel main sedimentary microfacies.
[0052] 2.2) Using the top and bottom depths of the target sand body as the range, analyze the range of rock physical parameters corresponding to different lithologies through rock physical intersection charts;
[0053] In this embodiment, cross-plot analysis revealed that the mudstone density of the sandstone unit in the example ranged from 2.25 g / cm³. 3 ~2.45g / cm 3 The longitudinal wave velocity ranges from 2650 m / s to 2900 m / s, and the sandstone density ranges from 2.05 g / cm³. 3 ~2.25g / cm 3 The longitudinal wave velocity ranges from 2440 m / s to 2650 m / s.
[0054] 2.3) Based on the analysis of well logging curves and the analysis of rock physical cross plots, determine the corresponding rock physical parameters for different sedimentary microfacies types;
[0055] 2.4) As Figure 3 As shown, based on the parameters obtained from the above steps, a single-well sand body mechanism model is established. A wavelet with the same dominant frequency as the seismic data is selected, a synthetic seismic record is produced, and the seismic attribute Attr(well) of the forward model record and the seismic attribute Attr'(well) of the actual well bypass are calculated. The correlation between the two is analyzed, and a linear or nonlinear calibration relationship is established. Then, the true amplitude attribute is calculated through the forward model amplitude attribute of the multi-parameter reservoir model.
[0056] In this embodiment, the seismic attribute extracted from the actual sand body case is the average amplitude attribute, and the established calibration chart is as follows: Figure 4 As shown, all the forward modeling records of drilled wells have a good linear correlation with the actual seismic properties, with a correlation coefficient of 0.82. This indicates that the parameters of the mechanism model are reasonably selected and can truly reflect the actual situation of the underground reservoir. The established relationship is Attr'=85.2*Attr+3438.3. Based on this relationship, the actual amplitude properties can be calculated from the forward modeling amplitude properties of the multi-parameter reservoir model.
[0057] In step 2.3) above, the corresponding petrophysical parameters for different sedimentary microfacies types are determined. Specifically, the main body and rim of the channel are predominantly sandstone, the overflow bank transitions from sandstone to mudstone, and the interchannel mudstone is purely mudstone. Considering the gradual transition between different sedimentary microfacies, and combining the density and velocity ranges of different microfacies from well logging analysis results, the corresponding petrophysical parameters are determined. For example, the density of the main channel is 2.1 g / cm³. 3 The longitudinal wave velocity is 2480 m / s, and the density at the riverbank is 2.2 g / cm³. 3 P-wave velocity 2600 m / s, overflow density 2.25 g / cm³ 3 The longitudinal wave velocity was 2700 m / s, and the density of the mudstone in the river channel was 2.4 g / cm³. 3 The longitudinal wave velocity is 2770 m / s.
[0058] In step 3) above, predicting the attribute threshold corresponding to the reservoir boundary based on the calibration relationship includes the following steps:
[0059] 3.1) Based on the sedimentary microfacies type covered by the target sand body, synthesize seismic records and convert the attribute values of the synthesized seismic records into the attribute values of the actual seismic data through calibration formulas;
[0060] 3.2) Based on the experience gained from implementing development wells in the field, clarify the definition of reservoir boundaries and the corresponding reservoir parameters;
[0061] In this embodiment, the target sand body was assessed through on-site directional and horizontal wells. If the thickness of the single-stage channel margin deposition and overflow deposition was less than 5m, or if the sand-to-soil ratio was less than 40% or the overflow thickness was greater than 80% when the formation thickness was less than 15m, the horizontal well assessment was poor and could be considered as the reservoir boundary.
[0062] 3.3) Based on the forward modeling results, determine the reservoir boundary definition and the corresponding attribute thresholds. For example... Figure 6a , Figure 6b As shown, according to the parameter definition of the reservoir boundary, when the actual amplitude attribute value is greater than -5000, it includes all the cases defined by the reservoir boundary. Therefore, -5000 can be set as the reservoir boundary threshold.
[0063] In step 3.1) above, the synthetic seismic record is created, specifically as follows:
[0064] (1) Combining the sedimentary microfacies types covered by the target sand body, the reservoir thickness is set as a single variable, and forward models of different sedimentary microfacies types are established and synthetic seismic records are produced. The attribute values of the synthetic seismic records are converted into the attribute values of the actual seismic data through calibration formulas.
[0065] (2) Combining the sedimentary microfacies types covered by the target sand body, the development thickness of different sedimentary microfacies and the possible combination relationship of sedimentary microfacies are set as variables. A reservoir model with multiple parameters of thickness and sedimentary microfacies combination is established so that it can cover various possible reservoir development conditions of the target sand body. Synthetic seismic records are produced, and the attribute values of the synthetic seismic records are converted into the attribute values of the actual seismic data through calibration relationship.
[0066] like Figure 5 The figure shown is a multi-parameter variable sand body mechanism model designed based on the target sand body example. The model considers the development and thickness variation of the sand body in a single phase. The thickness of the main channel is set to 0-20m, the thickness of the channel edge is set to 0-5m, and the thickness of the overflow bank is set to 0-5m. At the same time, the model considers the development and thickness of the sand body in two phases, as well as the changes in the microfacies combination relationship. The model designs the combination relationship of the upper overflow bank and the lower channel, the lower channel at the upper channel edge, and the upper and lower channels. The sand-to-land ratio distribution ranges from 0 to 1.
[0067] In step 4) above, guidance is provided for optimizing the development of horizontal wells, such as... Figure 7 The diagram shown is a schematic diagram of the lithological boundary of the target sand body instance. This diagram can provide a reference for the planning of development directional wells or horizontal wells. When deploying wells, they should be located within the reservoir boundary as much as possible to ensure the sandstone drilling rate of development wells and improve the implementation effect.
[0068] In one embodiment of the present invention, a reservoir boundary quantitative prediction system based on forward modeling of sand body mechanism is provided, comprising:
[0069] The first processing module determines the sand body mechanism model of typical well lines, clarifies the sedimentary microfacies types contained in the target sand body, the development scale of different sedimentary microfacies, and the possible combination relationships of sedimentary microfacies;
[0070] The second processing module uses well logging curves and rock physics cross plots to determine the rock physics parameters corresponding to different sedimentary microfacies types, simplifies all drilled well information into single-well sand body mechanism models, and performs seismic forward modeling analysis to obtain the calibration relationship between the actual seismic response and the forward modeling synthetic seismic record.
[0071] The threshold prediction module establishes a reservoir model with multiple parameters, including thickness and sedimentary microfacies, to cover various possible reservoir development conditions of the target sand body. It sets the reservoir parameter range corresponding to the reservoir boundary and predicts the attribute threshold corresponding to the reservoir boundary based on the calibration relationship.
[0072] The optimization module delineates reservoir boundaries based on attribute thresholds to guide the optimization of development horizontal wells.
[0073] In the above embodiments, the sand body mechanism model for typical well-connecting lines is determined, including:
[0074] Typical well profiles covering the target sand body area are selected from multiple directions. Based on the thickness of the sand body encountered in the drilled wells, the shape of the logging curves, and the range of values, the reservoir information encountered at the well points is extrapolated in combination with the lateral variation characteristics of the seismic response to obtain the sand body mechanism model of typical well lines.
[0075] In the above embodiments, the sedimentary microfacies types contained in the target sand body are clearly defined, including:
[0076] The sedimentary microfacies corresponding to the target sand body include four types: the main channel, the channel margin, the overflow bank, and the interchannel mudstone.
[0077] In the above embodiments, the calibration relationship between the actual seismic response and the forward-modeled synthetic seismic record includes:
[0078] Analyze the gamma, sonic, and density curves of the target sand body depth range and its vicinity in the drilled well, and infer the type and thickness of the microfacies encountered on the well based on the value range distribution and morphological changes of the curves.
[0079] Using the top and bottom depths of the target sand body as the range, we analyze the range of petrophysical parameters corresponding to different lithologies through petrophysical cross plots.
[0080] Based on the combined analysis of well logging curves and petrophysical cross plots, the corresponding petrophysical parameters for different sedimentary microfacies types were determined.
[0081] A single-well sand body mechanism model is established, a wavelet with the same dominant frequency as the seismic data is selected, a synthetic seismic record is produced, the seismic attributes of the forward model record and the seismic attributes of the actual well bypass are calculated, the correlation between the two is analyzed, a linear or nonlinear calibration relationship is established, and then the true amplitude attributes are calculated by forward modeling the amplitude attributes of the multi-parameter reservoir model.
[0082] In this embodiment, the corresponding petrophysical parameters for different sedimentary microfacies types are determined, including: the main body and edge of the channel are predominantly sandstone, the overflow bank is a transition from sandstone to mudstone, and the interchannel mudstone is pure mudstone. Considering the gradual relationship between different sedimentary microfacies, and combining the density and velocity ranges of different microfacies in the well logging curve analysis results, the corresponding petrophysical parameters for different microfacies are determined.
[0083] In the above embodiments, predicting the attribute threshold corresponding to the reservoir boundary based on the calibration relationship includes:
[0084] Based on the sedimentary microfacies type covered by the target sand body, a synthetic seismic record is generated, and the attribute values of the synthetic seismic record are converted into the attribute values of the actual seismic data through calibration formulas.
[0085] Based on the experience gained from implementing development wells in the field, the reservoir boundary definition and corresponding reservoir parameters are clearly defined;
[0086] Based on the forward modeling results, the reservoir boundary definition and the corresponding attribute thresholds are determined.
[0087] In this embodiment, the creation of a synthetic seismic record includes:
[0088] (1) Combining the sedimentary microfacies types covered by the target sand body, the reservoir thickness is set as a single variable, and forward models of different sedimentary microfacies types are established and synthetic seismic records are produced. The attribute values of the synthetic seismic records are converted into the attribute values of the actual seismic data through calibration formulas.
[0089] (2) Combining the sedimentary microfacies types covered by the target sand body, the development thickness of different sedimentary microfacies and the possible combination relationship of sedimentary microfacies are set as variables. A reservoir model with multiple parameters of thickness and sedimentary microfacies combination is established so that it can cover various possible reservoir development conditions of the target sand body. Synthetic seismic records are produced, and the attribute values of the synthetic seismic records are converted into the attribute values of the actual seismic data through calibration relationship.
[0090] The system provided in this embodiment is used to execute the above-described method embodiments. For specific processes and details, please refer to the above embodiments, which will not be repeated here.
[0091] In one embodiment of the present invention, a computing device structure is provided. This computing device can be a terminal, which may include: a processor, a communication interface, memory, a display screen, and an input device. The processor, communication interface, and memory communicate with each other via a communication bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and computer programs, which are executed by the processor to implement the aforementioned methods. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The communication interface is used for wired or wireless communication with external terminals. Wireless communication can be achieved through Wi-Fi, a management network, NFC (Near Field Communication), or other technologies. The display screen can be a liquid crystal display or an e-ink display. The input device can be a touch layer covering the display screen, or buttons, a trackball, or a touchpad mounted on the casing of the computing device, or an external keyboard, touchpad, or mouse. The processor can call logical instructions stored in the memory.
[0092] Furthermore, the logical instructions in the aforementioned memory can be implemented as software functional units and sold or used as independent products, and can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a 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 the present invention. 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.
[0093] In one embodiment of the present invention, a computer program product is provided, the computer program product including a computer program stored on a non-transitory computer-readable storage medium, the computer program including program instructions, and when the program instructions are executed by a computer, the computer is able to perform the methods provided in the above-described method embodiments.
[0094] In one embodiment of the present invention, a non-transitory computer-readable storage medium is provided, which stores server instructions that cause a computer to perform the methods provided in the above embodiments.
[0095] The computer-readable storage medium provided in the above embodiments has a similar implementation principle and technical effect to the above method embodiments, and will not be described again here.
[0096] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.
[0097] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.
[0098] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.
[0099] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims
1. A quantitative prediction method for reservoir boundaries based on forward modeling of sand body mechanisms, characterized in that, include: Determine the sand body mechanism model of typical well lines, clarify the sedimentary microfacies types contained in the target sand body, the development scale of different sedimentary microfacies, and the possible combination relationships of sedimentary microfacies; By using well logging curves and rock physics cross plots, the rock physics parameters corresponding to different sedimentary microfacies types are determined. All drilled well information is simplified into a single-well sand body mechanism model. Seismic forward modeling analysis is performed to obtain the calibration relationship between the actual seismic response and the forward modeled synthetic seismic record. Establish a reservoir model with multiple parameters and a combination of thickness and sedimentary microfacies to cover various possible reservoir development conditions of the target sand body, set the reservoir parameter range corresponding to the reservoir boundary, and predict the attribute threshold corresponding to the reservoir boundary based on the calibration relationship. Delineate reservoir boundaries based on attribute thresholds to guide the optimization of development horizontal wells; The calibration relationship between actual seismic response and forward-modeled synthetic seismic records includes: Analyze the gamma, sonic, and density curves of the target sand body depth range and its vicinity in the drilled well, and infer the type and thickness of the microfacies encountered in the well based on the value range distribution and morphological changes of the curves. Using the top and bottom depths of the target sand body as the range, we analyze the range of petrophysical parameters corresponding to different lithologies through petrophysical cross plots. Based on the combined analysis of well logging curves and petrophysical cross plots, the corresponding petrophysical parameters for different sedimentary microfacies types were determined. A single-well sand body mechanism model was established. A wavelet with the same dominant frequency as the seismic data was selected to create a synthetic seismic record. The seismic attribute Attr of the forward model record and the seismic attribute Attr' of the actual well bypass were calculated. The correlation between the two was analyzed, and linear or nonlinear calibration formulas were established. Then, the actual amplitude attribute was calculated by forward modeling the amplitude attribute of the multi-parameter reservoir model; where Attr'=85.
2. Attr+3438.3; Predicting attribute thresholds corresponding to reservoir boundaries based on calibration relationships, including: Based on the sedimentary microfacies type covered by the target sand body, a synthetic seismic record is generated, and the attribute values of the synthetic seismic record are converted into the attribute values of the actual seismic data through calibration formulas. Based on the experience gained from implementing development wells in the field, the reservoir boundary definition and corresponding reservoir parameters are clearly defined; Based on the forward modeling results, the reservoir boundary definition and corresponding attribute thresholds are determined: the reservoir boundary threshold is -5000.
2. The method for quantitative prediction of reservoir boundaries based on forward modeling of sand body mechanism as described in claim 1, characterized in that, Determine the sand body mechanism model for typical well-connected lines, including: Typical well profiles covering the target sand body area are selected from multiple directions. Based on the thickness of the sand body encountered in the drilled wells, the shape of the logging curves, and the range of values, the reservoir information encountered at the well points is extrapolated in combination with the lateral variation characteristics of the seismic response to obtain the sand body mechanism model of typical well lines.
3. The method for quantitative prediction of reservoir boundaries based on forward modeling of sand body mechanism as described in claim 1, characterized in that, Identify the sedimentary microfacies types contained in the target sand body, including: The sedimentary microfacies corresponding to the target sand body include four types: the main channel, the channel margin, the overflow bank, and the interchannel mudstone.
4. The method for quantitative prediction of reservoir boundaries based on forward modeling of sand body mechanism as described in claim 1, characterized in that, Determine the petrophysical parameters corresponding to different sedimentary microfacies types, including: The main body and edges of the river channel are predominantly sandstone, while the overflow bank transitions from sandstone to mudstone. The mudstone in the river channel is pure mudstone. Considering the gradual change between different sedimentary microfacies, and combining the density and velocity ranges of different microfacies in the well logging curve analysis results, the petrophysical parameters of different microfacies are determined.
5. The method for quantitative prediction of reservoir boundaries based on forward modeling of sand body mechanism as described in claim 1, characterized in that, Creating a synthetic seismic record includes: Based on the sedimentary microfacies types covered by the target sand body, reservoir thickness was set as a single variable. Forward models of different sedimentary microfacies types were established and synthetic seismic records were produced. The attribute values of the synthetic seismic records were converted into attribute values of the actual seismic data through calibration formulas. By combining the sedimentary microfacies types covered by the target sand body, the development thickness of different sedimentary microfacies and the possible combination relationships of sedimentary microfacies are set as variables. A reservoir model with multiple parameters of thickness and sedimentary microfacies combination is established so that it can cover various possible reservoir development situations of the target sand body. Synthetic seismic records are also produced, and the attribute values of the synthetic seismic records are converted into the attribute values of the actual seismic data through calibration formulas.
6. A reservoir boundary quantitative prediction system based on sand body mechanism model forward modeling, used to implement the reservoir boundary quantitative prediction method based on sand body mechanism model forward modeling as described in any one of claims 1 to 5, characterized in that, include: The first processing module determines the sand body mechanism model of typical well lines, clarifies the sedimentary microfacies types contained in the target sand body, the development scale of different sedimentary microfacies, and the possible combination relationships of sedimentary microfacies; The second processing module uses well logging curves and rock physics cross plots to determine the rock physics parameters corresponding to different sedimentary microfacies types, simplifies all drilled well information into single-well sand body mechanism models, and performs seismic forward modeling analysis to obtain the calibration relationship between the actual seismic response and the forward modeling synthetic seismic record. The threshold prediction module establishes a reservoir model with multiple parameters, including thickness and sedimentary microfacies, to cover various possible reservoir development conditions of the target sand body. It sets the reservoir parameter range corresponding to the reservoir boundary and predicts the attribute threshold corresponding to the reservoir boundary based on the calibration relationship. The optimization module delineates reservoir boundaries based on attribute thresholds to guide the optimization of development horizontal wells.
7. A computer-readable storage medium for storing one or more programs, characterized in that, The one or more programs include instructions that, when executed by a computing device, cause the computing device to perform any of the methods described in claims 1 to 5.
8. A computing device, characterized in that, include: One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions for performing any of the methods described in claims 1 to 5.