Prediction method, system and equipment for saline water layer in well drilling optimization design and medium
By conducting earthquake forwarding and inversion of drilling data, the distribution of the salt water layer is predicted, which solves the risk of failure caused by the salt water layer during drilling, and improves the prediction accuracy and drilling safety of the salt water layer.
Patent Information
- Application Number
- CN202311412691.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2023-10-27
- Publication Date
- 2025-04-29
AI Technical Summary
During the drilling process, the existence of the saline layer leads to failures such as well surge, well leakage, well collapse and drilling, which increases drilling safety risks, and the existing technology lacks effective saline layer prediction methods.
By obtaining the target logging data of the seismic channel, analyzing and processing is used to obtain the logging data of the target section of the saline layer, a forward model is established for seismic evolution, and then seismic inversion of fluid flow is performed to obtain the sensitive fluid factor of the saline layer, and finally the inversion results are used to predict the saline layer.
It improves the prediction accuracy of the brine layer, clarifies the distribution rules of the brine layer, supports drilling design, optimizes drilling trajectory, reduces the risk of drilling in the brine layer, and provides reliable engineering and technical guarantees.
Smart Images
Figure CN119902287A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of oil and gas exploration drilling, and more specifically, to a prediction method, system, equipment and medium for saltwater layers in drilling optimization design. Background Art
[0002] As a special reservoir, the unclear understanding of the formation mechanism and distribution range of saline layers has become an exploration obstacle. During the drilling process, saline layers will have a negative impact on exploration from drilling construction to rolling production. The existence of saline layers is prone to downhole failures such as well kicks, well leaks, well collapse and drill sticking, which brings great risks and challenges to drilling safety operations and has a significant impact on engineering operation safety and wellbore quality. Therefore, it is of great significance to explore the prediction method of saline layers.
[0003] During the drilling process in the Sichuan Basin, Tarim Basin and Bohai Bay Basin in my country, saline layers will be encountered, which poses a great challenge to drilling optimization. For example, the thickness of the gypsum rock in the Jialingjiang Formation in northeastern Sichuan varies, and saline layers exist locally. The reservoir type is mainly fracture-pore type, and the lithology is mainly dolomite or gypsum mixed with limestone. In addition, the vertical and horizontal distribution of the saline layer is unclear. Encountering the saline layer will increase the difficulty and risk of drilling. It is necessary to predict the saline layer to guide drilling design and field operations. At present, the prediction technology for saline layers is very lacking, and targeted prediction methods and technologies have not yet been formed. Summary of the invention
[0004] The purpose of the present invention is to provide a prediction method, system, equipment and medium for salt water layers in drilling optimization design, which can provide reliable engineering technology guarantee for exploration and rolling development and avoid the negative impact caused by drilling into salt water layers.
[0005] The above technical objectives of the present invention are achieved through the following technical solutions:
[0006] In a first aspect, an embodiment of the present application provides a method for predicting a saltwater layer in a drilling optimization design, comprising the following steps:
[0007] S1, obtaining target logging data of one of the seismic channels in the work area to be tested, analyzing and processing the target logging data, and obtaining logging data of the target layer section of the saline layer corresponding to the target logging data, wherein the logging data of the target layer section of the saline layer represents the logging response characteristics of the saline layer encountered during the drilling process of the seismic channel;
[0008] S2, performing seismic forward modeling on the well logging data of the target layer section of the saline layer to obtain a forward model corresponding to the well logging data of the target layer section of the saline layer;
[0009] S3, performing seismic inversion of fluid mobility on the forward model to obtain inversion results, which include mobility factors sensitive to the saltwater layer;
[0010] S4, performing seismic inversion on the seismic record data of the seismic trace using the inversion result to obtain a saltwater layer prediction result corresponding to the seismic trace.
[0011] The beneficial effects of the present invention are as follows: in the above-mentioned processing steps, the data of the logging response characteristics of the saline layer are fully utilized, so the forward model established by seismic forward modeling using the logging data of the target layer section of the saline layer is more in line with reality, and the brine layer-sensitive mobility factor of the inversion result in the forward model is also more accurate. The brine layer is predicted by the fluid fluidity of the brine layer and the seismic wave dispersion effect caused by the fluid fluidity, so that the distribution law of the brine layer can be clarified, and finally the prediction accuracy of the brine layer can be improved; and the prediction results of the brine layer can be used to support drilling design, optimize drilling trajectory, reduce the risk of drilling into the brine layer, so as to provide reliable engineering technology guarantee for exploration and rolling development, and avoid the negative impact caused by drilling into the brine layer.
[0012] Based on the above technical solution, the present invention can also be improved as follows.
[0013] Furthermore, the above method also includes:
[0014] Steps S1 to S4 are processed in sequence for each seismic trace in the work area to be measured, and a three-dimensional salt water layer prediction result corresponding to the work area to be measured is obtained.
[0015] The beneficial effect of adopting the above further scheme is: the advantage is that the three-dimensional salt water layer prediction results of the entire work area to be tested can be obtained, providing support for the optimal design of drilling.
[0016] Furthermore, the above-mentioned target logging data includes at least two items of logging compressional wave velocity, logging shear wave velocity and logging density.
[0017] The beneficial effect of adopting the above further scheme is: combining multiple data in the logging data for processing and comprehensively analyzing to obtain the prediction results of the salt water layer, which can make the prediction results more accurate.
[0018] Furthermore, the above target logging data includes a plurality of sub-data under different temperature and pressure conditions.
[0019] The beneficial effect of adopting the above further scheme is: combining the target logging data under different temperatures and pressures, and taking into account the impact of changes in temperature and pressure on the data of the brine layer. On the one hand, it can make the established forward model more reasonable and reliable, and on the other hand, it can further improve the prediction accuracy of the brine layer.
[0020] In a second aspect, an embodiment of the present application provides a prediction system for a saltwater layer in a drilling optimization design, which is applied to a prediction method for a saltwater layer in a drilling optimization design in any one of the first aspects, including:
[0021] The parsing module is used to obtain the target logging data of one of the seismic channels in the work area to be tested, and parse the target logging data to obtain the logging data of the target layer section of the saline layer corresponding to the target logging data. The logging data of the target layer section of the saline layer represents the logging response characteristics of the saline layer encountered during the drilling process of the seismic channel;
[0022] A forward modeling module is used to perform seismic forward modeling on the well logging data of the target layer section of the saline layer to obtain a forward model corresponding to the well logging data of the target layer section of the saline layer;
[0023] An inversion module is used to perform seismic inversion of fluid mobility on the forward model to obtain inversion results, which include mobility factors sensitive to saline layers;
[0024] The test module is used to perform seismic inversion on the seismic record data of the seismic trace using the inversion result to obtain the salt water layer prediction result corresponding to the seismic trace.
[0025] Furthermore, the above system also includes:
[0026] The integration module is used to process each seismic trace in the work area to be tested in sequence through the analysis module, forward modeling module, inversion module and test module to obtain the three-dimensional salt water layer prediction result corresponding to the work area to be tested.
[0027] Furthermore, in the above-mentioned analysis module, the target logging data includes at least two items of logging P-wave velocity, logging S-wave velocity and logging density.
[0028] Furthermore, in the above-mentioned analysis module, the target logging data includes a plurality of sub-data under different temperature and pressure conditions.
[0029] In a third aspect, an embodiment of the present application provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements any one of the methods in the first aspect when executing the computer program.
[0030] In a fourth aspect, an embodiment of the present application provides a non-transitory computer-readable storage medium, which stores computer instructions, and the computer instructions enable a computer to execute any one of the methods in the first aspect.
[0031] Compared with the prior art, the present invention has at least the following beneficial effects:
[0032] In the scheme of the present application, full use is made of the data of the logging response characteristics of the saline layer. Therefore, the forward model established by seismic forward modeling using the logging data of the target layer section of the saline layer is more realistic, and the brine layer-sensitive mobility factor of the inversion result in the forward model is also more accurate. The brine layer is predicted by the fluid fluidity of the brine layer and the seismic wave dispersion effect caused by the fluid fluidity, which can clarify the distribution pattern of the brine layer and ultimately improve the prediction accuracy of the brine layer. The prediction results of the brine layer can be used to support drilling design, optimize drilling trajectory, and reduce the risk of drilling into the brine layer, so as to provide reliable engineering technical guarantees for exploration and rolling development and avoid the negative impact caused by drilling into the brine layer.
[0033] In the scheme of the present application, multiple data in the logging data are processed and comprehensively analyzed to obtain the prediction results of the brine layer. At the same time, the target logging data at different temperatures and pressures are combined to consider the impact of changes in temperature and pressure on the data of the brine layer. On the one hand, the established forward model can be made more reasonable and reliable, and on the other hand, the prediction accuracy of the brine layer can be further improved. BRIEF DESCRIPTION OF THE DRAWINGS
[0034] The drawings described herein are used to provide a further understanding of the embodiments of the present invention, constitute a part of this application, and do not constitute a limitation of the embodiments of the present invention. In the drawings:
[0035] Figure 1 is a graph of well logging data in an embodiment of the present invention;
[0036] Figure 2 A schematic diagram of a forward model and a saltwater layer prediction result in an embodiment of the present invention;
[0037] Figure 3 It is a schematic diagram of the saltwater layer prediction result obtained by processing the through-well seismic profile in the work area according to an embodiment of the present invention;
[0038] Figure 4 It is a schematic plan view of the prediction result of the salt water layer in the working area in the embodiment of the present invention;
[0039] Figure 5 A method flow chart of a prediction method in an embodiment of the present invention;
[0040] Figure 6 A connection diagram of a prediction system in an embodiment of the present invention;
[0041] Figure 7 Schematic diagram of the connection of electronic equipment in an embodiment of the present invention. DETAILED DESCRIPTION
[0042] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.
[0043] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.
[0044] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.
[0045] In the description of the embodiments of the present invention, "plurality" means at least 2.
[0046] Example 1
[0047] This embodiment provides a method for predicting saltwater layers in drilling optimization design, such as Figure 5 As shown, the following steps are included:
[0048] S1, obtaining the target logging data of one of the seismic channels in the work area to be tested, analyzing and processing the target logging data, and obtaining the logging data of the target layer section of the saline layer corresponding to the target logging data. The logging data of the target layer section of the saline layer represents the logging response characteristics of the saline layer encountered during the drilling process of the seismic channel.
[0049] Among them, the target logging data includes multiple data such as longitudinal wave velocity, shear wave velocity and density. Comprehensive calculation of multiple data can make the prediction results more accurate.
[0050] Optionally, the above-mentioned target logging data includes at least two items of logging longitudinal wave velocity, logging shear wave velocity and logging density.
[0051] Specifically, the process of analyzing the target logging data is completed by professional interpreters and can be expressed by the following formula:
[0052] S=Analyse(v p ,v s ,ρ,…);
[0053] Where S is the logging data of the target layer of the saltwater layer, Analyse(·) means the analysis of the data by implicit function formula, and v p 、v s and ρ are the logging P-wave velocity, S-wave velocity and density, respectively.
[0054] S2, performing seismic forward modeling on the well logging data of the target layer section of the saline layer to obtain a forward model corresponding to the well logging data of the target layer section of the saline layer.
[0055] Among them, the processing process of seismic forward modeling can be expressed by the following formula:
[0056] M = F (well);
[0057] Where M represents the result of seismic forward modeling, F(·) represents seismic forward modeling using implicit function formula, and well represents the well logging data of the target layer of the saline layer.
[0058] Specifically, the above steps make full use of the data of logging response characteristics of the saline layer, so the forward model established by seismic forward modeling using logging data of the target layer section of the saline layer is more practical.
[0059] Optionally, the above target logging data includes a plurality of sub-data under different temperature and pressure conditions.
[0060] Among them, by combining the target logging data at different temperatures and pressures as mentioned above, the influence of changes in temperature and pressure on the data of the brine layer is taken into consideration. On the one hand, the established forward model can be made more reasonable and reliable, and on the other hand, the prediction accuracy of the brine layer can be further improved.
[0061] S3, performing seismic inversion of fluid mobility on the forward model to obtain inversion results, which include mobility factors that are sensitive to the saltwater layer.
[0062] Among them, the processing process of seismic inversion can be expressed by the following formula:
[0063] f = Inversion(M);
[0064] Where f represents the inversion result of seismic inversion of the forward model, Inversion(·) represents the seismic inversion of the forward model using the implicit function formula, and M represents the result of seismic forward modeling (forward model).
[0065] Among them, through seismic inversion, the seismic wave dispersion effect caused by fluid mobility can be used to select the brine layer sensitive mobility factor, and this brine layer sensitive mobility factor can be used for the inversion of actual seismic recording data, so as to obtain the prediction accuracy of the brine layer in each seismic channel.
[0066] S4, performing seismic inversion on the seismic record data of the seismic trace using the inversion result to obtain a saltwater layer prediction result corresponding to the seismic trace.
[0067] Specifically, the seismic inversion is performed again on the seismic trace corresponding to the inversion result through the above-mentioned inversion result, so as to obtain the prediction result of the saline layer in the seismic trace; the processing of each seismic trace in the work area to be tested is also the same, so the saline layer prediction result of each seismic trace in the work area to be tested can be obtained in this way, and the three-dimensional saline layer prediction result of the work area to be tested can be obtained after integrating the saline layer prediction results of each seismic trace, so as to provide support for the optimal design of drilling, avoid encountering the saline layer during the drilling process, and reduce the drilling risk.
[0068] Optionally, the above method further includes:
[0069] Steps S1 to S4 are processed in sequence for each seismic trace in the work area to be measured, and a three-dimensional salt water layer prediction result corresponding to the work area to be measured is obtained.
[0070] Specifically, taking the high sulfur content area in northeastern Sichuan as an example, the reservoir in this area is mainly developed in the Jialingjiang Formation, the reservoir type is mainly fracture-pore type, and the lithology is mainly dolomite or gypsum intercalated with limestone. In order to clarify the distribution of this set of salt water layers, the salt water layers in this area are predicted by the processing method in this application; Figure 1 As shown, Figure 1 is a graph of well logging data, Figure 1 From left to right are the curves of P-wave velocity, density and P-wave impedance. The depth range of the three data is 2900-3950m, while the saline reservoir is mainly located in the depth range of 3000-3100m, showing low density and low impedance characteristics. The forward model and prediction results can be found in Figure 2 ,exist Figure 2 The left figure is a schematic diagram of the model established using the logging data of the target layer of the salt water layer. The model parameters are shown in Table 1. The salt water layer is mainly located in the middle of the second layer, and its velocity changes with frequency. This is mainly due to the dispersion of seismic waves caused by the flow of salt water. Figure 2 The middle figure in the figure is the seismic record obtained by forward modeling and diffusion-viscosity wave equation migration. It can be seen that the top and bottom interfaces of the salt water layer show strong energy reflection, time delay and phase distortion. Due to the influence of the seismic wave dispersion effect, the elastic interface below the reservoir also shows obvious amplitude attenuation and phase distortion. Figure 2 The right figure in the figure is the brine layer fluidity anomaly calculated using the seismic record data in the middle figure. From this figure, it can be seen that there is an obvious strong fluid fluidity anomaly at the location of the brine layer, indicating that the fluid mobility at this location is strong. This attribute can well indicate the location and spatial distribution of the brine layer and is not affected by the elastic layer anomaly.
[0071] Table 1
[0072]
[0073] Specifically, Figure 3 The seismic profile of the area and its predicted saline layer anomaly results are shown in Figure 3 The black line inserted in the figure is the well trajectory curve. It is found through the drilling process record that Figure 3 The black dashed box in a encountered a saltwater layer. Figure 3 b is the calculated salt water layer anomaly. It can be seen that there are many strong fluid mobility anomalies in the black dotted box, which are highly consistent with the drilled salt water layer, indicating that the salt water layer prediction using this method has reliable accuracy.
[0074] in, Figure 4 The plane prediction results of the saltwater layer predicted for this area are shown in Figure 4 In the figure, the black dotted line marks the area with strong fluid mobility anomaly, which is the potential distribution range of the brine layer in the target layer. Combining geological and drilling analysis, it can be seen that the brine layer is mainly developed in the southeast direction of the well drilling location in the high-steep area, and the wells that encounter the brine layer are also closer to this area. Therefore, it is more likely to encounter the brine layer in this area.
[0075] Example 2
[0076] The present application embodiment provides a prediction system for salt water layer in drilling optimization design, which is applied to the prediction method for salt water layer in drilling optimization design in any one of the embodiments 1, such as Figure 6 As shown, including:
[0077] The analysis module is used to obtain the target logging data of one of the seismic channels in the work area to be measured, analyze the target logging data, and obtain the logging data of the target layer section of the saline layer corresponding to the target logging data. The logging data of the target layer section of the saline layer represents the logging response characteristics of the saline layer encountered during the drilling process of the seismic channel.
[0078] Optionally, in the above analysis module, the target logging data includes at least two items of logging longitudinal wave velocity, logging shear wave velocity and logging density.
[0079] Optionally, in the above analysis module, the target logging data includes a plurality of sub-data under different temperature and pressure conditions.
[0080] The forward modeling module is used to perform seismic forward modeling on the logging data of the target layer section of the saline layer to obtain the forward model corresponding to the logging data of the target layer section of the saline layer.
[0081] The inversion module is used to perform seismic inversion of fluid mobility in the forward model to obtain inversion results, which include mobility factors that are sensitive to saline layers.
[0082] The test module is used to perform seismic inversion on the seismic record data of the seismic trace using the inversion result to obtain the salt water layer prediction result corresponding to the seismic trace.
[0083] Optionally, the above system further includes:
[0084] The integration module is used to process each seismic trace in the work area to be tested in sequence through the analysis module, forward modeling module, inversion module and test module to obtain the three-dimensional salt water layer prediction result corresponding to the work area to be tested.
[0085] Example 3
[0086] The present application embodiment provides an electronic device, such as Figure 7 As shown, it includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, any method in Embodiment 1 is implemented.
[0087] Example 4
[0088] An embodiment of the present application provides a non-transitory computer-readable storage medium, which stores computer instructions, and the computer instructions enable a computer to execute any method in Example 1.
[0089] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.
Claims
1. A method for predicting saline layers in drilling optimization design, characterized in that: The following steps are involved: S1, obtaining target logging data of one of the seismic channels in the work area to be tested, analyzing and processing the target logging data to obtain logging data of a target layer section of a saline layer corresponding to the target logging data, wherein the logging data of the target layer section of the saline layer represents the logging response characteristics of the saline layer encountered during the drilling process of the seismic channel; S2, performing seismic forward modeling on the well logging data of the target layer section of the salt water layer to obtain a forward model corresponding to the well logging data of the target layer section of the salt water layer; S3, performing seismic inversion of fluid mobility on the forward model to obtain an inversion result, wherein the inversion result includes a mobility factor sensitive to the saltwater layer; S4, performing seismic inversion on the seismic record data of the seismic trace using the inversion result to obtain a saltwater layer prediction result corresponding to the seismic trace.
2. The method for predicting saltwater layers in drilling optimization design according to claim 1, characterized in that: The method further comprises: Steps S1 to S4 are sequentially processed for each seismic trace in the work area to be measured to obtain a three-dimensional salt water layer prediction result corresponding to the work area to be measured.
3. The method for predicting saltwater layers in drilling optimization design according to claim 1, characterized in that: The target logging data includes at least two items of logging compressional wave velocity, logging shear wave velocity and logging density.
4. The method for predicting saltwater layers in drilling optimization design according to claim 1, characterized in that: The target logging data includes a plurality of sub-data under different temperature and pressure conditions.
5. A system for predicting saline layers in drilling optimization design, applied to a method for predicting saline layers in drilling optimization design according to any one of claims 1 to 4, characterized in that: include: The parsing module is used to obtain the target logging data of one of the seismic channels in the work area to be tested, and to parse and process the target logging data to obtain the logging data of the target layer section of the salt water layer corresponding to the target logging data, wherein the logging data of the target layer section of the salt water layer represents the logging response characteristics of the salt water layer encountered during the drilling process of the seismic channel; A forward modeling module, used for performing seismic forward modeling on the well logging data of the target layer section of the salt water layer to obtain a forward model corresponding to the well logging data of the target layer section of the salt water layer; An inversion module, used for performing seismic inversion of fluid mobility on the forward model to obtain an inversion result, wherein the inversion result includes a mobility factor sensitive to the saltwater layer; The testing module is used to perform seismic inversion on the seismic record data of the seismic trace using the inversion result to obtain the salt water layer prediction result corresponding to the seismic trace.
6. The prediction system for saltwater layers in drilling optimization design according to claim 5, characterized in that: The system further comprises: The integration module is used to process each seismic trace in the work area to be tested by the analysis module, the forward modeling module, the inversion module and the test module in sequence to obtain the three-dimensional salt water layer prediction result corresponding to the work area to be tested.
7. The prediction system for saltwater layers in drilling optimization design according to claim 5, characterized in that: In the analysis module, the target logging data includes at least two items of logging compressional wave velocity, logging shear wave velocity and logging density.
8. The prediction system for saltwater layers in drilling optimization design according to claim 5, characterized in that: In the analysis module, the target logging data includes a plurality of sub-data under different temperature and pressure conditions.
9. An electronic device, characterized in that: The method comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the method according to any one of claims 1 to 4 is implemented when the processor executes the computer program.
10. A non-transitory computer-readable storage medium, characterized in that: The non-transitory computer-readable storage medium stores computer instructions, which enable a computer to execute the method of any one of claims 1-4.