Water flooded layer productivity prediction method and system based on saturation logging in four sub-jackets
By using the four-neutron in-cascade saturation logging technology, the production capacity of water-flooded layers can be predicted, which solves the problem of quantitative prediction of production capacity of water-flooded layers, improves the prediction accuracy, and provides a basis for the optimal selection of fracturing sites.
Patent Information
- Application Number
- CN202411040034.3
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-07-31
- Publication Date
- 2026-02-03
AI Technical Summary
Existing technologies make it difficult to effectively predict the production capacity of water-flooded layers, especially in the mid-to-late development stages of domestic oilfields, where quantitative evaluation of production capacity prediction for water-flooded layers has not yet been achieved.
A method based on four-neutron casing saturation logging was adopted. By performing power-law fitting between the four-neutron casing saturation sensitive parameter and the actual daily oil production of multiple wells already in production in water-flooded reservoirs, a water-flooded reservoir production capacity prediction equation was established. This equation was then used to predict the daily oil production of wells to be put into production.
This method enables rapid and quantitative prediction of the production capacity of water-flooded layers, improves prediction accuracy, and provides a basis for the optimal selection of subsequent fracturing sites.
Smart Images
Figure CN121451925A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of water-flooded layer productivity prediction technology, and in particular to a method and system for predicting water-flooded layer productivity based on four-neutron casing saturation logging. Background Technology
[0002] Reservoir productivity is a comprehensive indicator for evaluating the dynamic characteristics of a reservoir. Productivity prediction, in particular, is a technique for comprehensively evaluating the fluid production capacity of a reservoir. The results of reservoir productivity prediction can verify oil and gas exploration findings and provide the most fundamental basis for oil and gas field development. Previously, reservoir parameters obtained using well logging methods primarily reflected the static characteristics of the reservoir. However, four-neutron in-casing saturation logging data can evaluate remaining oil and analyze productivity, transforming the analysis from static to dynamic. Therefore, four-neutron in-casing saturation logging data plays a significant role in reservoir productivity evaluation.
[0003] Many scholars have explored the application of well logging curves for production capacity prediction. In summary, they have used the envelope area of well logging curves or multiple well logging curves to establish a relationship with oil testing and production data. CN201810273202.1, published on October 11, 2019, describes a method that uses well logging parameters such as natural gamma, spontaneous potential, and interpreted porosity data to construct a method that reflects reservoir production capacity parameters based on the envelope area between curves. Wei Ruling (Research on Well Logging Production Capacity Evaluation Technology of Deep Sandstone and Conglomerate Reservoirs [D]. Dongying: China University of Petroleum, 2010) uses nuclear magnetic resonance logging to evaluate reservoir pore structure and constructs a comprehensive classification index based on multiple pore structure parameters to classify reservoir production capacity for evaluation. Zeng Jingbo's "Production Capacity Prediction Method Based on Low-Permeability Reservoir Quality Evaluation," Journal of Yangtze University (Natural Science Edition), Vol. 14, No. 7, 2017, proposes a production capacity prediction method for low-permeability reservoirs based on reservoir quality evaluation and development needs, establishing a relationship between a comprehensive evaluation index and a production index per meter.
[0004] The aforementioned methods for predicting production capacity in newly explored strata have yielded good results. However, most domestic oilfields have entered the mid-to-late stages after decades of rolling development, requiring techniques such as water injection to increase reservoir pressure and maintain production capacity. Therefore, a large number of reservoirs are currently water-flooded, making it impossible to predict water-flooded layers using the methods described above. Regarding water-flooded layers, some have attempted to evaluate remaining reservoir oil using casing saturation logging data, but this remains largely semi-quantitative, and further quantitative evaluation is needed.
[0005] Therefore, there is an urgent need to provide a technical solution to address the above problems. Summary of the Invention
[0006] To address the aforementioned technical problems, this invention provides a method and system for predicting the productivity of water-flooded layers based on four-neutron in-cascade saturation logging.
[0007] Firstly, the present invention provides a method for predicting the productivity of water-flooded formations based on four-neutron casing saturation logging, the technical solution of which is as follows:
[0008] The water-flooded reservoir was selected as the study area, and the power-law fitting was performed on the sensitive parameters of the four-neutron casing saturation of multiple wells in the study area and the actual daily oil production to obtain the water-flooded reservoir production prediction equation.
[0009] By substituting the sensitive parameters of the neutron saturation within the casing of the wells to be put into production in the study area into the water-flooded layer production prediction equation, the predicted daily oil production of the wells to be put into production is obtained.
[0010] The beneficial effects of the water-flooded layer productivity prediction method based on four-neutron casing saturation logging of the present invention are as follows:
[0011] The method of this invention not only enables rapid quantitative prediction of the productivity of water-flooded layers, but also improves the prediction accuracy of the productivity of water-flooded layers, providing a basis for the subsequent selection of fracturing sites.
[0012] Based on the above scheme, the water-flooded layer productivity prediction method based on four-neutron in-cascade saturation logging of the present invention can be further improved as follows.
[0013] In one alternative approach, the step of obtaining the four-neutron casing saturation sensitive parameter for any producing well within the study area includes:
[0014] Obtain core analysis data from any of the put-in-production wells and perform four-neutron in-casing saturation logging on the put-in-production well to obtain the four-neutron in-casing saturation processing parameters for the put-in-production well.
[0015] Using the core analysis data of any of the wells already in production, the saturation processing parameters of the four-neutron casing of the well in production are calibrated to obtain the target processing parameters of the well in production that are highly applicable to the study area.
[0016] Using the target processing parameters of any of the wells already in production, the well is subjected to four-neutron casing saturation processing to obtain the oil saturation and effective porosity of the well. Based on the product between the oil saturation and effective porosity of the well, the four-neutron casing saturation sensitive parameter of the well is obtained.
[0017] In one alternative approach, it also includes:
[0018] Substitute the saturation-sensitive parameters of the four neutrons within the casing of multiple wells already in production within the study area into the water-flooded layer production prediction equation to obtain the predicted daily oil production of each well already in production.
[0019] Calculate the error between the predicted daily oil production and the actual daily oil production of each well already in production. When the error value corresponding to each well already in production is less than the threshold, perform the step of substituting the saturation sensitive parameter of the four neutrons in the well to be put into production in the study area into the water-flooded layer production capacity prediction equation to obtain the predicted daily oil production of the well to be put into production.
[0020] In one alternative approach, it also includes:
[0021] When the error value corresponding to any well already in production is not less than the threshold, the water-flooded layer production capacity prediction equation is corrected until the error value corresponding to each well already in production is less than the threshold. Then, the step of substituting the neutron saturation sensitive parameter of the well to be put into production in the study area into the water-flooded layer production capacity prediction equation is executed to obtain the predicted daily oil production of the well to be put into production.
[0022] Secondly, this invention provides a water-flooded layer productivity prediction system based on four-neutron in-cascade saturation logging. The technical solution of this system is as follows:
[0023] Includes: a processing module and a prediction module;
[0024] The processing module is used to: select the water-flooded reservoir as the study area, and perform power fitting between the sensitive parameters of the four-neutron casing saturation of multiple wells already in production within the study area and the actual daily oil production to obtain the water-flooded reservoir production capacity prediction equation.
[0025] The prediction module is used to: substitute the sensitive parameters of the saturation of the four neutrons within the well to be put into production in the study area into the water-flooded layer production capacity prediction equation to obtain the predicted daily oil production of the well to be put into production.
[0026] The beneficial effects of the water-flooded layer productivity prediction system based on four-neutron casing saturation logging of the present invention are as follows:
[0027] The system of this invention not only enables rapid quantitative prediction of the productivity of water-flooded layers, but also improves the prediction accuracy of the productivity of water-flooded layers, providing a basis for the subsequent selection of fracturing sites.
[0028] Based on the above scheme, the water-flooded layer productivity prediction system based on four-neutron in-cascade saturation logging of the present invention can be further improved as follows.
[0029] In an alternative embodiment, the method further includes a preprocessing module; the preprocessing module is used for:
[0030] Obtain core analysis data from any of the put-in-production wells and perform four-neutron in-casing saturation logging on the put-in-production well to obtain the four-neutron in-casing saturation processing parameters for the put-in-production well.
[0031] Using the core analysis data of any of the wells already in production, the saturation processing parameters of the four-neutron casing of the well in production are calibrated to obtain the target processing parameters of the well in production that are highly applicable to the study area.
[0032] Using the target processing parameters of any of the wells already in production, the well is subjected to four-neutron casing saturation processing to obtain the oil saturation and effective porosity of the well. Based on the product between the oil saturation and effective porosity of the well, the four-neutron casing saturation sensitive parameter of the well is obtained.
[0033] In one alternative embodiment, the method further includes: a first verification module; the first verification module is used for:
[0034] Substitute the saturation-sensitive parameters of the four neutrons within the casing of multiple wells already in production within the study area into the water-flooded layer production prediction equation to obtain the predicted daily oil production of each well already in production.
[0035] Calculate the error between the predicted daily oil production and the actual daily oil production for each well that has been put into production. When the error value for each well that has been put into production is less than the threshold, the prediction module is invoked.
[0036] In one alternative embodiment, the method further includes: a second verification module; the second verification module is used for:
[0037] When the error value corresponding to any well already in production is not less than the threshold, the production capacity prediction equation of the flooded layer is corrected until the error value corresponding to each well already in production is less than the threshold, at which point the prediction module is invoked.
[0038] Thirdly, the technical solution of an electronic device according to the present invention is as follows:
[0039] It includes a memory, a processor, and a program stored in the memory and running on the processor, wherein the processor executes the program to implement the steps of the water-flooded layer productivity prediction method based on four-neutron casing saturation logging of the present invention.
[0040] Fourthly, the technical solution of a computer-readable storage medium provided by the present invention is as follows:
[0041] The computer-readable storage medium stores instructions that, when read, cause the computer-readable storage medium to perform the steps of the water-flooded layer productivity prediction method based on four-neutron in-cascade saturation logging of the present invention.
[0042] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it in accordance with the contents of the specification, and in order to make the above and other objects, features and advantages of the present invention more apparent and understandable, specific embodiments of the present invention are described below. Attached Figure Description
[0043] The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:
[0044] Figure 1 This is a schematic flowchart of an embodiment of the method for predicting the productivity of water-flooded layers based on four-neutron in-cascade saturation logging according to the present invention.
[0045] Figure 2 A cross-plot of the saturation-sensitive parameters within the four-neutron housing and the actual daily oil production;
[0046] Figure 3 This is a cross-plot of actual daily oil production and predicted daily oil production.
[0047] Figure 4 This is a schematic diagram of an embodiment of a water-flooded layer productivity prediction system based on four-neutron in-cascade saturation logging according to the present invention.
[0048] Figure 5 This is a schematic diagram of an embodiment of an electronic device according to the present invention. Detailed Implementation
[0049] Exemplary embodiments of the invention will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention can be implemented in various forms and should not be limited to the embodiments set forth herein.
[0050] Figure 1 This diagram illustrates a flowchart of an embodiment of a water-flooded layer productivity prediction method based on four-neutron in-casing saturation logging provided by the present invention. This method can be executed by electronic devices such as terminal devices or servers. The terminal device can be any fixed or mobile terminal, such as user equipment (UE), mobile device, user terminal, terminal, cellular phone, cordless phone, personal digital assistant (PDA), handheld device, computing device, vehicle-mounted device, or wearable device. The server can be a single server or a server cluster consisting of multiple servers. Any electronic device can implement the water-flooded layer productivity prediction method based on four-neutron in-casing saturation logging by having its processor call computer-readable instructions stored in its memory. Figure 1 As shown, it includes the following steps:
[0051] S1. Select the water-flooded reservoir as the study area, and perform power fitting between the sensitive parameters of the four-neutron casing saturation of multiple wells already in production within the study area and the actual daily oil production to obtain the water-flooded reservoir production prediction equation.
[0052] The study area includes water-flooded reservoirs, such as those in the Da'an Honggang area. Other areas can be selected based on actual conditions, without restrictions. Newly completed wells within the study area are considered as "produced wells." The production capacity prediction equation for water-flooded reservoirs is a functional relationship between the sensitive parameter of the four-neutron casing saturation of produced wells and their daily oil production. Daily production is the production capacity, expressed in tons per day.
[0053] It should be noted that the flooded layer production capacity prediction equation is obtained by power-law fitting in this embodiment. The flooded layer production capacity prediction equation is: RCOY=A*IB; A and B are both constants obtained through fitting; RCOY is the predicted daily oil production, in tons / day.
[0054] S2. Substitute the sensitive parameters of the neutron saturation within the casing of the well to be put into production in the study area into the water-flooded layer production capacity prediction equation to obtain the predicted daily oil production of the well to be put into production.
[0055] The wells awaiting production and those already in production all belong to the same research area.
[0056] In one alternative approach, the step of obtaining the four-neutron casing saturation sensitive parameter of any produced well in the same water-flooded layer within the study area includes:
[0057] Obtain core analysis data from any of the wells already in production, and perform four-neutron in-casing saturation logging on the well to obtain the four-neutron in-casing saturation processing parameters for the well.
[0058] Among them, four-neutron in-casing saturation logging is a logging technique that uses neutron-neutron and neutron-gamma dual physical processes to evaluate reservoir lithology, physical properties, and oil-bearing capacity. Research has shown that the oil saturation of four-neutron in-casing saturation can characterize reservoir oil-bearing capacity. Combined with effective porosity, a good correlation is established with daily oil production, fitting a production capacity equation to achieve the effect of production capacity prediction, providing the most basic basis for oil and gas field development.
[0059] Using core analysis data from any of the wells already in production, the neutron saturation processing parameters within the casing of the well are calibrated to obtain the target processing parameters that are highly applicable to the study area.
[0060] The target processing parameters are those for the widely applicable neutron-cascade saturation logging.
[0061] Using the target processing parameters of any of the wells already in production, the well is subjected to four-neutron casing saturation processing to obtain the oil saturation and effective porosity of the well. Based on the product between the oil saturation and effective porosity of the well, the four-neutron casing saturation sensitive parameter of the well is obtained.
[0062] The process involves obtaining reservoir characteristic data from target processing parameters for feature analysis, acquiring oil saturation and effective porosity, and then using the product of oil saturation and effective porosity of already produced wells to obtain the four-neutron casing saturation sensitive parameter, i.e., the mobile fluid index. Specifically: I = QEP * SO; I is the four-neutron casing saturation sensitive parameter (mobile fluid index), QEP is the effective porosity in %; SO is the oil saturation in %.
[0063] In one alternative approach, it also includes:
[0064] Calculate the error between the predicted daily oil production and the actual daily oil production for each well that has been put into production. When the error value for each well that has been put into production is less than the threshold, execute S1.
[0065] The actual daily oil production can be obtained from the client's production data. The default threshold is 0.2, but it can be set according to actual needs; no limit is set here.
[0066] In one alternative approach, it also includes:
[0067] When the error value corresponding to any well already in production is not less than the threshold, the water-flooded layer production capacity prediction equation is corrected until the error value corresponding to each well already in production is less than the threshold. Then, the step of substituting the neutron saturation sensitive parameter of the well to be put into production in the study area into the water-flooded layer production capacity prediction equation is executed to obtain the predicted daily oil production of the well to be put into production.
[0068] When the error value corresponding to any well already in production is not less than the threshold, the production capacity prediction equation of the flooded layer is corrected until the error value corresponding to each well already in production is less than the threshold, then S1 is executed.
[0069] Among them, the constant term of the flooded layer production capacity prediction equation can be iteratively corrected based on the judgment results until the error between the predicted daily oil production and the actual daily oil production is less than the threshold.
[0070] To better illustrate the technical solution of this embodiment, the following example is used. Specifically, the research area is a certain block in the Da'an Honggang area.
[0071] S10. Select a water-flooded reservoir in a certain block of Da'an Honggang area as the study area. Based on the sensitive parameters of the four-neutron casing saturation of multiple wells in the study area and the actual daily oil production, perform power fitting to obtain the water-flooded reservoir production capacity prediction equation.
[0072] Among them, based on the sensitive parameter I of the neutron casing saturation of multiple wells already in production within the study area and the actual daily oil production RCO, according to the cross-plot method (such as... Figure 2 (As shown) and a univariate Nth power regression model was established for power fitting to obtain the flooded layer capacity prediction equation: RCOY=0.0000001*I2.4318.
[0073] It should be noted that, from Figure 2 As can be seen from the data, the oil saturation and effective porosity obtained from the saturation of the four-neutron casing are related to the daily oil production. The movable fluid index is obtained by multiplying the oil saturation and effective porosity. The larger the movable fluid index, the higher the single-well production capacity.
[0074] S20. Substitute the saturation-sensitive parameters of the four neutrons within the casing of the multiple wells already in production within the study area into the water-flooded layer production prediction equation to obtain the predicted daily oil production of each well already in production.
[0075] S30. Calculate the error between the predicted daily oil production and the actual daily oil production of each well already in production. When the error value corresponding to each well already in production is less than the threshold, substitute the sensitive parameter of the saturation of the four neutrons in the well to be put into production in the study area into the water-flooded layer production capacity prediction equation to obtain the predicted daily oil production of the well to be put into production.
[0076] The predicted and actual daily oil production of several wells already in production are shown in Table 1 below. Figure 3 The diagram shows a cross plot of the predicted daily oil production (RCOY) and the actual daily oil production (RCO) of wells already in production. Regression analysis shows the correlation coefficient R. 2 Greater than 0.9. Taking well H202 as an example, the effective porosity QET of well H202 is 16.8%, and the oil saturation SO is 55%. Substituting into the formula I = QET * SO, we get I = 924. Substituting I into the water-flooded layer production prediction equation, we calculate RCOY as 1.629 tons / day. The actual daily oil production is 1.7 tons / day, and the absolute error (error value) is 0.07 tons / day, which is less than 0.2 tons / day.
[0077] Table 1:
[0078]
[0079] The technical solution of this embodiment analyzes and studies the saturation-sensitive parameters within the four-neutron casing, determines that the mobile fluid index is the productivity-sensitive parameter for water-flooded layers, and combines the actual daily oil production of the water-flooded layer with the daily oil production of the production well and the mobile fluid index to derive a productivity equation. This allows for productivity prediction of single wells in the water-flooded layer, and the application of the productivity equation yields the productivity prediction results for the reservoir in the study area. This achieves rapid and quantitative prediction of the productivity of water-flooded layers, providing a basis for the subsequent selection of fracturing layers.
[0080] Figure 4 This diagram illustrates a structural schematic of an embodiment of a water-flooded layer productivity prediction system 200 based on four-neutron in-cascade saturation logging provided by the present invention. Figure 4 As shown, the system 200 includes: a processing module 210 and a prediction module 220;
[0081] The processing module 210 is used to: select the water-flooded reservoir as the study area, and perform power fitting between the sensitive parameters of the four-neutron casing saturation of multiple wells already in production within the study area and the actual daily oil production to obtain the water-flooded reservoir production capacity prediction equation.
[0082] The prediction module 220 is used to: substitute the sensitive parameters of the neutron saturation within the casing of the well to be put into production in the study area into the water-flooded layer production capacity prediction equation to obtain the predicted daily oil production of the well to be put into production.
[0083] In an alternative embodiment, the method further includes a preprocessing module; the preprocessing module is used for:
[0084] Obtain core analysis data from any of the put-in-production wells and perform four-neutron in-casing saturation logging on the put-in-production well to obtain the four-neutron in-casing saturation processing parameters for the put-in-production well.
[0085] Using the core analysis data of any of the wells already in production, the saturation processing parameters of the four-neutron casing of the well in production are calibrated to obtain the target processing parameters of the well in production that are highly applicable to the study area.
[0086] Using the target processing parameters of any of the wells already in production, the well is subjected to four-neutron casing saturation processing to obtain the oil saturation and effective porosity of the well. Based on the product between the oil saturation and effective porosity of the well, the four-neutron casing saturation sensitive parameter of the well is obtained.
[0087] In one alternative embodiment, the method further includes: a first verification module; the first verification module is used for:
[0088] Substitute the saturation-sensitive parameters of the four neutrons within the casing of multiple wells already in production within the study area into the water-flooded layer production prediction equation to obtain the predicted daily oil production of each well already in production.
[0089] Calculate the error between the predicted daily oil production and the actual daily oil production of each well that has been put into production. When the error value corresponding to each well that has been put into production is less than the threshold, the prediction module 220 is invoked.
[0090] In one alternative embodiment, the method further includes: a second verification module; the second verification module is used for:
[0091] When the error value corresponding to any well already in production is not less than the threshold, the production capacity prediction equation of the flooded layer is corrected until the error value corresponding to each well already in production is less than the threshold, at which point the prediction module 220 is invoked.
[0092] The technical solution of this embodiment not only realizes rapid quantitative prediction of the production capacity of the water-flooded layer, but also improves the prediction accuracy of the production capacity of the water-flooded layer, providing a basis for the subsequent selection of fracturing sites.
[0093] The parameters and steps for implementing the corresponding functions of each module in the water-flooded layer productivity prediction system 200 based on four-neutron in-casing saturation logging in this embodiment can be referred to the parameters and steps in the embodiments of the water-flooded layer productivity prediction method based on four-neutron in-casing saturation logging above, and will not be repeated here.
[0094] like Figure 5 As shown, an electronic device 300 according to an embodiment of the present invention includes a processor 320 coupled to a memory 310. The memory 310 stores at least one computer program 330, which is loaded and executed by the processor 320 to enable the electronic device 300 to implement any of the above-mentioned methods for predicting the productivity of water-flooded layers based on four-neutron casing saturation logging. Specifically:
[0095] The electronic device 300 can vary considerably due to differences in configuration or performance. It may include one or more processors 320 (Central Processing Units, CPUs) and one or more memories 310. The memories 310 store at least one computer program 330, which is loaded and executed by the processors 320 to enable the electronic device 300 to implement any of the water-flooded layer productivity prediction methods based on four-neutron in-cascade saturation logging provided in the above embodiments. Of course, the electronic device 300 may also have wired or wireless network interfaces, a keyboard, and input / output interfaces for input and output. It may also include other components for implementing device functions, which will not be elaborated upon here.
[0096] An embodiment of the present invention provides a computer-readable storage medium storing at least one computer program, which is loaded and executed by a processor to enable a computer to implement any of the above-mentioned methods for predicting the productivity of water-flooded layers based on four-neutron casing saturation logging.
[0097] Alternatively, the computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a compact disc read-only memory (CD-ROM), magnetic tape, a floppy disk, and an optical data storage device, etc.
[0098] In an exemplary embodiment, a computer program product or computer program is also provided, which includes computer instructions stored in a computer-readable storage medium. A processor of an electronic device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the electronic device to perform any of the above-described methods for predicting the productivity of water-flooded layers based on four-neutron in-cascade saturation logging.
[0099] It should be noted that the terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and represent a limitation on a specific order or sequence. Where appropriate, the order of use for similar objects can be interchanged so that the embodiments of this application described herein can be implemented in an order other than that shown or described.
[0100] Those skilled in the art will recognize that this invention can be implemented as a system, method, or computer program product. Therefore, this disclosure can be specifically implemented in the following forms: it can be entirely hardware, entirely software (including firmware, resident software, microcode, etc.), or a combination of hardware and software, generally referred to herein as a "circuit," "module," or "system." Furthermore, in some embodiments, this invention can also be implemented as a computer program product in one or more computer-readable media containing computer-readable program code.
[0101] Any combination of one or more computer-readable media can be used. A computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this application, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.
[0102] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.
Claims
1. A method for predicting the productivity of water-flooded formations based on four-neutron casing saturation logging, characterized in that, include: The water-flooded reservoir was selected as the study area, and the power-law fitting was performed on the sensitive parameters of the four-neutron casing saturation of multiple wells in the study area and the actual daily oil production to obtain the water-flooded reservoir production prediction equation. By substituting the sensitive parameters of the neutron saturation within the casing of the wells to be put into production in the study area into the water-flooded layer production prediction equation, the predicted daily oil production of the wells to be put into production is obtained.
2. The method for predicting the productivity of water-flooded formations based on four-neutron casing saturation logging according to claim 1, characterized in that, The steps for obtaining the sensitive parameters of the neutron casing saturation of any well already in production within the study area include: Obtain core analysis data from any of the put-in-production wells and perform four-neutron in-casing saturation logging on the put-in-production well to obtain the four-neutron in-casing saturation processing parameters for the put-in-production well. Using the core analysis data of any of the wells already in production, the saturation processing parameters of the four-neutron casing of the well in production are calibrated to obtain the target processing parameters of the well in production that are highly applicable to the study area. Using the target processing parameters of any of the wells already in production, the well is subjected to four-neutron casing saturation processing to obtain the oil saturation and effective porosity of the well. Based on the product between the oil saturation and effective porosity of the well, the four-neutron casing saturation sensitive parameter of the well is obtained.
3. The method for predicting the productivity of water-flooded formations based on four-neutron casing saturation logging according to claim 1, characterized in that, Also includes: Substitute the saturation-sensitive parameters of the four neutrons within the casing of multiple wells already in production within the study area into the water-flooded layer production prediction equation to obtain the predicted daily oil production of each well already in production. Calculate the error between the predicted daily oil production and the actual daily oil production of each well already in production. When the error value corresponding to each well already in production is less than the threshold, perform the step of substituting the saturation sensitive parameter of the four neutrons in the well to be put into production in the study area into the water-flooded layer production capacity prediction equation to obtain the predicted daily oil production of the well to be put into production.
4. The method for predicting the productivity of water-flooded layers based on four-neutron casing saturation logging according to claim 3, characterized in that, Also includes: When the error value corresponding to any well already in production is not less than the threshold, the water-flooded layer production capacity prediction equation is corrected until the error value corresponding to each well already in production is less than the threshold. Then, the step of substituting the neutron saturation sensitive parameter of the well to be put into production in the study area into the water-flooded layer production capacity prediction equation is executed to obtain the predicted daily oil production of the well to be put into production.
5. A water-flooded formation productivity prediction system based on four-neutron in-cascade saturation logging, characterized in that, include: Processing module and prediction module; The processing module is used to: select the water-flooded reservoir as the study area, and perform power fitting between the sensitive parameters of the four-neutron casing saturation of multiple wells already in production within the study area and the actual daily oil production to obtain the water-flooded reservoir production capacity prediction equation. The prediction module is used to: substitute the sensitive parameters of the saturation of the four neutrons within the well to be put into production in the study area into the water-flooded layer production capacity prediction equation to obtain the predicted daily oil production of the well to be put into production.
6. The water-flooded formation productivity prediction system based on four-neutron casing saturation logging according to claim 5, characterized in that, Also includes: Preprocessing module; The preprocessing module is used for: Obtain core analysis data from any of the put-in-production wells and perform four-neutron in-casing saturation logging on the put-in-production well to obtain the four-neutron in-casing saturation processing parameters for the put-in-production well. Using the core analysis data of any of the wells already in production, the saturation processing parameters of the four-neutron casing of the well in production are calibrated to obtain the target processing parameters of the well in production that are highly applicable to the study area. Using the target processing parameters of any of the wells already in production, the well is subjected to four-neutron casing saturation processing to obtain the oil saturation and effective porosity of the well. Based on the product between the oil saturation and effective porosity of the well, the four-neutron casing saturation sensitive parameter of the well is obtained.
7. The water-flooded formation productivity prediction system based on four-neutron casing saturation logging according to claim 5, characterized in that, Also includes: First verification module; the first verification module is used for: Substitute the saturation-sensitive parameters of the four neutrons within the casing of multiple wells already in production within the study area into the water-flooded layer production prediction equation to obtain the predicted daily oil production of each well already in production. Calculate the error between the predicted daily oil production and the actual daily oil production for each well that has been put into production. When the error value for each well that has been put into production is less than the threshold, the prediction module is invoked.
8. The water-flooded formation productivity prediction system based on four-neutron casing saturation logging according to claim 7, characterized in that, Also includes: The second verification module; the second verification module is used for: When the error value corresponding to any well already in production is not less than the threshold, the production capacity prediction equation of the flooded layer is corrected until the error value corresponding to each well already in production is less than the threshold, at which point the prediction module is invoked.
9. An electronic device, characterized in that, The electronic device includes a processor coupled to a memory, the memory storing at least one computer program, which is loaded and executed by the processor to enable the electronic device to implement the water-flooded layer productivity prediction method based on four-neutron casing saturation logging as described in any one of claims 1 to 4.
10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores at least one computer program, which is loaded and executed by a processor to enable the computer-readable storage medium to implement the water-flooded layer productivity prediction method based on four-neutron casing saturation logging as described in any one of claims 1 to 4.
Citation Information
Patent Citations
Method and system for constructing logging parameters reflecting reservoir productivity
CN110320573A