Productivity prediction method based on dynamic data constraint reservoir classification and related device
By acquiring dynamic data through PLT production logging, dividing the fluid-contributing zones, and constructing a micro-oil production index prediction model, the problem of difficult production capacity evaluation in deep-water carbonate reservoirs was solved, and high-accuracy production capacity prediction was achieved.
Patent Information
- Application Number
- CN202411043269.8
- 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
In deep-water carbonate reservoirs, conventional production capacity assessment methods are difficult to accurately determine reservoir production capacity due to the lack of segmented dynamic data, which leads to difficulties in reservoir value assessment and exploration and development.
Dynamic data is obtained through PLT production logging, production contribution zones are divided, the micron-oil recovery index is determined, and a micron-oil recovery index prediction model for different reservoir space types is constructed. Combined with reservoir classification, the production capacity of the study well is calculated.
It improves the accuracy and applicability of production capacity prediction, and can effectively evaluate production capacity when there are no DST wells or a small number of DST wells.
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Figure CN121456570A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application relates to the field of oil exploration, and in particular to a dynamic data-constrained reservoir classification productivity prediction method and related device. BACKGROUND
[0002] Oil testing and productivity evaluation are of great significance in the oil exploration and development stages. In the exploration stage, DST oil testing is generally performed on exploration wells and key evaluation wells to evaluate single-well productivity and reservoir scale. In the development stage, productivity evaluation is a key factor affecting deepwater "few wells with high production", and determines the development scale and investment value of an oilfield. Conventional productivity evaluation is generally determined based on the flow pressure difference and production obtained through DST oil testing. In wells without DST oil testing, the correlation between the data of tested wells and permeability is established to obtain productivity.
[0003] However, for carbonate reservoirs with strong heterogeneity, the percolation law is complex, and the number of DST testing wells in deepwater exploration areas is relatively small. In addition, general whole-section testing is generally used, and there is a lack of corresponding segmented testing dynamic data, so it is difficult to establish the correlation between productivity and permeability.
[0004] Therefore, in the development stage, the conventional productivity evaluation method has poor application effect in deepwater carbonate reservoirs, and cannot well determine the reservoir productivity, which brings great difficulties to subsequent reservoir value evaluation and exploration and development. SUMMARY
[0005] In view of the above problems, the purpose of the present application is to provide a dynamic data-constrained reservoir classification productivity prediction method and related device.
[0006] In a first aspect, an embodiment of the present application provides a dynamic data-constrained reservoir classification productivity prediction method, comprising:
[0007] dividing the PLT production logging test interval into a plurality of liquid production contribution intervals according to the analysis results of the dynamic data of a plurality of wells subjected to PLT production logging;
[0008] determining the corresponding metering oil index of each liquid production contribution interval;
[0009] determining the reservoir storage space type of each liquid production contribution interval;
[0010] constructing a metering oil index prediction model of reservoirs with different reservoir storage space types;
[0011] determining the reservoir classification of the research layer series in the research well;
[0012] determining the reservoir storage space type in different reservoir classification intervals and the metering oil index prediction model corresponding to the different reservoir classification intervals;
[0013] According to the prediction model of the oil recovery index, the prediction oil recovery index of the different reservoir classification sections is determined.
[0014] According to the reservoir classification and the prediction oil recovery index, the productivity of the research well is determined.
[0015] In one embodiment, the productivity of the research well is determined according to the comprehensive oil recovery index J0 of the research well.
[0016] The comprehensive oil recovery index of the research well is used to characterize the productivity of the research well.
[0017] The comprehensive oil recovery index J0 of the research well is calculated according to the following formula:
[0018]
[0019] Wherein, J0 is the comprehensive oil recovery index.
[0020] i is the reservoir classification section number;
[0021] n is the total number of reservoir classification sections;
[0022] h i is the thickness of the i-th reservoir classification section;
[0023] q i is the prediction oil recovery index of the i-th reservoir classification section.
[0024] In one embodiment, the oil recovery index of the fluid production contribution section is calculated according to the following formula:
[0025]
[0026] Wherein, PI is the oil recovery index; Q o is the oil production on the fluid production contribution section; H is the effective thickness of the oil layer; P e is the formation pressure; P f is the flowing pressure.
[0027] In one embodiment, the determination method of the reservoir space type is as follows:
[0028] According to the core, wall core, thin section, mercury injection data and completion interpretation results table of the PLT production logging test section, and the obtained logging curves of the PLT production logging test section, the fracture, pore development and pore throat structure characteristics of the test section are determined.
[0029] According to the liquid production contribution capacity of the multiple liquid production contribution intervals of the test interval, and fracture, pore development and pore throat structure characteristics of the test interval, a reservoir space type affecting the liquid production contribution capacity of the reservoir is determined.
[0030] In one embodiment, the reservoir space type includes: fracture, fracture-cave type and pore type.
[0031] In one embodiment, for the fracture, fracture-cave type reservoir, a corresponding Mi production index prediction model is constructed, including:
[0032] A sensitive parameter affecting the fracture, fracture-cave type reservoir is determined;
[0033] According to the sensitive parameter, a first parameter RQI affecting the quality of the fracture, fracture-cave type reservoir is constructed k ;
[0034] A crossplot of the first parameter and the corresponding Mi production index is established;
[0035] According to the crossplot, a Mi production index prediction model of the fracture, fracture-cave type reservoir is established.
[0036] In one embodiment, the sensitive parameter affecting the fracture, fracture-cave type reservoir includes NMR effective porosity and NMR permeability;
[0037] The construction of the first parameter RQI affecting the quality of the fracture, fracture-cave type reservoir k , includes:
[0038] According to the NMR effective porosity and the NMR permeability, a quality factor and a breakthrough coefficient are determined;
[0039] According to the quality factor and the breakthrough coefficient, the first parameter is determined;
[0040] The first parameter is calculated according to the following formula:
[0041] RQI k = RQI × T k ;
[0042] Wherein: RQI k is the first parameter; RQI is the reservoir quality factor; T k is the breakthrough coefficient.
[0043] In one embodiment, for the pore type reservoir, a corresponding Mi production index prediction model is constructed, including:
[0044] A sensitive parameter affecting the pore type reservoir is determined;
[0045] According to the sensitive parameters, a second parameter SRQI affecting reservoir quality of the pore type is constructed;
[0046] A crossplot of the second parameter and a corresponding recovery factor is established;
[0047] According to the crossplot, a recovery factor prediction model of the reservoir of the pore type is established.
[0048] In one embodiment, the sensitive parameters affecting the reservoir of the pore type include a nuclear magnetic effective porosity, a nuclear magnetic permeability and an oil saturation;
[0049] The second parameter SRQI is calculated according to the following formula:
[0050]
[0051] Wherein, S o is the oil saturation; φ is the nuclear magnetic effective porosity; RQI is a reservoir quality factor, and the RQI is determined according to the nuclear magnetic effective porosity and the nuclear magnetic permeability.
[0052] In a second aspect, an embodiment of the present application provides a device for predicting productivity of reservoir classification based on dynamic data, comprising:
[0053] A fluid production contribution interval division module is configured to divide the PLT production logging test interval into a plurality of fluid production contribution intervals according to an analysis result of dynamic data of a plurality of wells subjected to PLT production logging.
[0054] A recovery factor determination module is configured to determine a recovery factor corresponding to each of the fluid production contribution intervals.
[0055] A reservoir space type determination module is configured to determine a reservoir space type of each of the fluid production contribution intervals.
[0056] A prediction model construction module is configured to construct a recovery factor prediction model of a reservoir having a different reservoir space type.
[0057] A reservoir classification determination module is configured to determine a reservoir classification of a research interval in a research well.
[0058] A productivity determination module is configured to determine a reservoir space type in each of different reservoir classification intervals, and a recovery factor prediction model corresponding to each of the different reservoir classification intervals; determine a predicted recovery factor of each of the different reservoir classification intervals according to the recovery factor prediction model; and determine a productivity of the research well according to the reservoir classification and the predicted recovery factor.
[0059] In a third aspect, an embodiment of the present application provides a computing device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the productivity prediction method of the dynamic data-constrained reservoir classification when executing the program.
[0060] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, which stores a computer program, wherein the computer program is executable on a processor to implement the productivity prediction method of the dynamic data-constrained reservoir classification.
[0061] In a fifth aspect, an embodiment of the present application provides a computer program product, comprising a computer program, wherein the computer program is executable on a processor to implement the productivity prediction method of the dynamic data-constrained reservoir classification.
[0062] The above technical solution provided by the embodiments of the present application has at least the following beneficial effects:
[0063] The embodiments of the present application provide a productivity prediction method of dynamic data-constrained reservoir classification and related devices, in which, according to the interpretation and analysis results of the dynamic data of a plurality of wells that have performed PLT production logging in the exploration stage of an oilfield, a productivity index prediction model of reservoirs of different reservoir space types is constructed; in the development stage, reservoir classification is performed on the research layer system in the research well; and the dynamic data of the PLT production logging and the reservoir classification results are combined to form reservoir classification with dynamic data constraint. According to the constructed productivity index prediction model and the reservoir classification in the research well, the productivity of the research well can be determined. The productivity prediction method provided by the embodiments of the present application has high accuracy and good applicability, is convenient for popularization and application, and effectively overcomes the shortcomings of being unable to perform productivity evaluation or poor productivity evaluation effect in the case of no DST test well or few DST test wells.
[0064] The technical solutions of the present application will be further described in detail below with reference to the accompanying drawings and embodiments. BRIEF DESCRIPTION OF DRAWINGS
[0065] The accompanying drawings are used to provide a further understanding of the present application, and constitute a part of the specification, and are used to explain the present application together with embodiments of the present application, and do not constitute a limitation on the present application. In the drawings:
[0066] Figure 1 is a flowchart of the productivity prediction method of dynamic data-constrained reservoir classification in the embodiments of the present application;
[0067] Figure 2 is the first parameter RQI in the embodiments of the present application k and the productivity index crossplot;
[0068] Figure 3 Figure 1 is a cross plot of nuclear magnetic permeability and reservoir thickness in an embodiment of the present application;
[0069] Figure 4 Figure 2 is a cross plot of the second parameter SRQI and the oil recovery index in an embodiment of the present application;
[0070] Figure 5 Figure 3 is a well logging column chart of a research well in a certain basin in an embodiment of the present application;
[0071] Figure 6 Figure 4 is a structural schematic diagram of a productivity prediction device of dynamic data constrained reservoir classification in an embodiment of the present application. DETAILED DESCRIPTION
[0072] The embodiment provides a productivity prediction method of dynamic data constrained reservoir classification and related devices, although the exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.
[0073] The embodiment of the present application provides a productivity prediction method of dynamic data constrained reservoir classification, referring to Figure 1, the method comprises the following steps: Figure 1
[0074] S11, according to the analysis result of the dynamic data of a plurality of wells which have been subjected to PLT production logging, the PLT production logging test interval is divided into a plurality of liquid production contribution intervals.
[0075] S12, the oil recovery index corresponding to each liquid production contribution interval is determined.
[0076] S13, the reservoir storage space type of each liquid production contribution interval is determined.
[0077] S14, the oil recovery index prediction model of the reservoir with different reservoir storage space types is constructed.
[0078] S15, the reservoir classification of the research layer system in the research well is determined.
[0079] S16, the reservoir storage space type in different reservoir classification intervals and the oil recovery index prediction model corresponding to the different reservoir classification intervals are determined; the predicted oil recovery index of the different reservoir classification intervals is determined according to the oil recovery index prediction model; the productivity of the research well is determined according to the reservoir classification and the predicted oil recovery index.
[0080] The embodiment of the present application provides a productivity prediction method based on dynamic data and reservoir classification, uses dynamic data obtained from a plurality of wells with tested PLT (Production Logging Test) production logging in the exploration stage, combines conventional logging curves to establish a productivity index prediction model suitable for different reservoir space types; classifies reservoirs of a research layer system in a research well in the development stage; solves productivity of the research well through dynamic data-constrained reservoir classification, and further realizes productivity grading and quantitative prediction.
[0081] In step S11, in the exploration stage, a plurality of wells with PLT production logging are selected for a research layer in a research area, PLT interpretation analysis is performed on the selected wells, for each well, a test interval is divided into a plurality of liquid production contribution intervals in the vertical direction, and liquid production contribution capacity on each liquid production contribution interval is evaluated. Therefore, all liquid production contribution intervals corresponding to all wells with PLT production logging can be obtained.
[0082] In step S12, for each well, according to the size of the liquid production contribution capacity on different liquid production contribution intervals in each well, the yield of each well is split in different liquid production contribution intervals, and the productivity indices of different liquid production contribution intervals are calculated according to the flowing pressure and the formation pressure.
[0083] In one embodiment, for each well with PLT production logging, the productivity indices of different liquid production contribution intervals corresponding to the well can be calculated according to the following formula:
[0084]
[0085] Wherein, PI is the productivity index; Q o is the oil production on the liquid production contribution interval; H is the effective thickness of the oil layer; P e is the formation pressure; P f is the flowing pressure.
[0086] In step S13, the reservoir space types of each liquid production contribution interval divided above are determined respectively.
[0087] In one embodiment, the determination method of the reservoir space type is as follows:
[0088] According to the core, wall core, slice, mercury injection data and completion interpretation results table of the test interval of the PLT production logging, and the obtained logging curves of the test interval of the PLT production logging, the fracture, pore development and pore throat structure characteristics of the test interval are evaluated;
[0089] According to the liquid production contribution capacity of the test interval to the multiple liquid production contribution intervals, and the fracture, pore development and pore throat structure characteristics of the test interval, the reservoir space type affecting the liquid production contribution capacity of the reservoir is determined.
[0090] In one embodiment, the PLT production logging test interval core, wall core, slice, mercury injection data and completion interpretation results are static data. The obtained logging curves of the test interval of the PLT production logging are conventional logging curves, including: caliper (CAL) curve, natural gamma (GR) curve, density (DEN) curve, neutron (CNL) curve, nuclear magnetic effective porosity (φ) curve, nuclear magnetic permeability (K) curve, oil saturation (S o ) curve, electrical imaging data and acoustic imaging data, etc.
[0091] In one embodiment, the obtained conventional logging curves of the test interval of the PLT production logging are preprocessed, quality analyzed and standardized to remove the effects of hole enlargement, outliers and system deviations caused by different logging instruments.
[0092] The inventors found that, by comprehensively considering the liquid production contribution capacity of different liquid production contribution intervals, and the fracture, pore development and pore throat structure characteristics of the evaluation, the reservoir space type can be determined as the main control factor of the liquid production contribution capacity of the reservoir without considering dynamic factors such as engineering reconstruction and work system.
[0093] In one embodiment, the reservoir space type includes fracture, fracture-cave type and pore type. The reservoirs of the two types of reservoir space are typical high-yield layers, and the fracture-cave type reservoir has obvious layer suppression due to the existence of dominant channels. Therefore, according to the new idea of “block, layer system, reservoir space” hierarchical division and evaluation, different Mi production index prediction models are established according to different reservoir space types.
[0094] In step S14, next, for different reservoir space types, Mi production index prediction models of the corresponding reservoirs are constructed.
[0095] In one embodiment, for the fracture-cave type reservoir, the sensitive parameters affecting the fracture-cave type reservoir are determined to construct the first parameter RQI k affecting the quality of the fracture-cave type reservoir. The first parameter of each liquid contribution interval is taken as the abscissa, and the Mi production index is taken as the ordinate, to establish the crossplot of the first parameter and the Mi production index. According to the mathematical relationship between the first parameter and the Mi production index in the above crossplot, the Mi production index prediction model of the fracture-cave type reservoir is established.
[0096] The inventors discovered that, due to the presence of advantageous channels in fractured and pitted reservoirs, the permeability and porosity of these reservoirs are superior to those of the reservoirs above and below them. Therefore, in one embodiment, the nuclear magnetic resonance effective porosity φ and nuclear magnetic resonance permeability K can be selected as sensitive parameters.
[0097] Based on the identified sensitive parameters, the reservoir quality factor RQI (Reservoir Quality Index) characterizing reservoir quality and the breakthrough coefficient T, reflecting reservoir permeability and heterogeneity, are calculated. k .
[0098] The reservoir quality factor (RQI) is calculated using the following formula:
[0099]
[0100] Where K is the NMR permeability, mD; and φ is the NMR effective porosity, a decimal value.
[0101] Advance coefficient T k Calculate according to the following formula:
[0102]
[0103] Among them, K max Explain the maximum permeability of each segment of the PLT, mD; The average permeability of each layer of the PLT is explained in mD.
[0104] Based on the obtained quality factor and breakthrough coefficient, a new parameter, namely the first parameter RQI, is established to comprehensively reflect the reservoir quality of fractured and fracture-vuggy reservoirs. k The formula for calculating the first parameter is as follows:
[0105] RQI k =RQI×T k ;
[0106] Among them: RQI k The first parameter is RQI, which is the reservoir quality factor, and T is the reservoir quality factor. k This is the surge coefficient.
[0107] In one embodiment, for porous reservoir types, corresponding sensitive parameters are determined, and these sensitive parameters are used to construct a second parameter, SRQI, that affects the quality of porous reservoirs. Using the calculated second parameter and the Micron-Enhanced Oil Index (MEI) for each liquid contribution segment as coordinate values, a cross-plot of the second parameter and MEI is established. Based on the mathematical relationship between the second parameter and MEI in the cross-plot, a MEI prediction model for porous reservoir types is established.
[0108] For the pore type reservoir, the inventors, according to the characteristics of the reservoir, in one embodiment, select the nuclear magnetic effective porosity φ, the nuclear magnetic permeability K and the oil saturation S o as the sensitive parameters.
[0109] According to the determined sensitive parameters, a new parameter, i.e. the second parameter SRQI, is constructed to comprehensively reflect the quality of the pore type reservoir. The second parameter is calculated according to the following formula:
[0110]
[0111] wherein RQI is the reservoir quality factor, determined according to the nuclear magnetic effective porosity and the nuclear magnetic permeability; S o is the oil saturation, a decimal; mD; φ is the nuclear magnetic effective porosity, a decimal.
[0112] Accordingly, the following is obtained: under two different reservoir space types, the first parameter RQI k and the second parameter SRQI are taken as the independent variables to construct the oil production index prediction model.
[0113] In step S15, the reservoir of the research interval of the well to be researched is classified, for example, the reservoir classification is divided according to the reservoir classification standard, mainly for the later development stage or the evaluation stage.
[0114] In step S16, according to the oil production index prediction model obtained in steps S11-S14 and the reservoir classification divided in step S15, the productivity of the research well is determined.
[0115] In one embodiment, the inventors construct the reservoir comprehensive oil production index J0characterizing the productivity of the research well.
[0116] The expression of the reservoir comprehensive oil production index of the research well is as follows:
[0117]
[0118] wherein J0is the reservoir comprehensive oil production index;
[0119] i is the reservoir classification section number;
[0120] n is the total number of reservoir classification sections;
[0121] h i is the thickness of the i-th reservoir classification section;
[0122] q i is the predicted oil production index of the i-th reservoir classification section.
[0123] Specifically, the reservoir is classified by step S15, and the study layer series of the study well is divided into multiple different reservoir classifications and reservoir classification sections; according to the method in steps S11-S14, the reservoir space types corresponding to different reservoir classification sections in the study well are determined, and different reservoir space types are selected to correspond to the oil recovery index prediction model.
[0124] According to the selected different oil recovery index prediction models, the first parameter and the second parameter in the corresponding reservoir classification section are determined, and then the specific predicted oil recovery index values corresponding to different reservoir classification sections are determined (i.e., the first parameter and the second parameter are substituted into the oil recovery index prediction model, respectively), and the productivity of the study well is calculated according to the formula of the comprehensive oil recovery index of the reservoir.
[0125] The following is a specific example of an oilfield, and the specific implementation steps are as follows:
[0126] (1) For the target layer of the study area, 11 wells that have undergone PLT production logging are selected, and PLT interpretation and analysis are performed, and 78 liquid production contribution sections are divided in total from the 11 PLT production logs, and the liquid production contribution capacity of different liquid production contribution sections in the vertical direction is determined and evaluated. According to the oil recovery index formula, the oil recovery index corresponding to each liquid production contribution section is calculated.
[0127] (2) Obtain the conventional logging curves of the test sections of the 11 PLT production logs, which include caliper (CAL) curves, natural gamma (GR) curves, density (DEN) curves, neutron (CNL) curves, nuclear magnetic effective porosity (φ) curves, nuclear magnetic permeability (K) curves, oil saturation (S o ) curves, electrical imaging data, and acoustic imaging data, etc. In order to remove the effects of wellbore expansion, outliers, and systematic biases caused by different logging instruments, the obtained conventional logging curves are preprocessed, quality analyzed, and standardized.
[0128] (3) The conventional logging curves of the test sections of the above PLT production logs are combined with the static data of the test sections, such as core, wall core, thin section, mercury injection data, and completion interpretation results, to determine and evaluate the fracture, pore development, and pore throat structure characteristics of the test sections.
[0129] (4) According to the obtained liquid production contribution capacity of multiple liquid production contribution sections of the test sections, and the fracture, pore development, and pore throat structure characteristics of the test sections, the reservoir space types affecting the liquid production contribution capacity of the 78 liquid production contribution sections are determined. In the 78 liquid production contribution sections in this example, there are 16 liquid production contribution sections corresponding to the fracture, fracture-cave type reservoir space type, and 62 liquid production contribution sections corresponding to the pore type reservoir space type.
[0130] (5) For the determined fracture and fracture-cave type reservoir, the nuclear magnetic effective porosity φ and the nuclear magnetic permeability K are preferred as sensitive parameters to calculate the first parameter RQI k . The crossplot of the first parameter and the oil recovery factor is established, and it is shown from the figure that the first parameter RQI Figure 2 k is positively correlated with the oil recovery factor, and there is a certain partition. According to the relationship between the first parameter and the oil recovery factor shown in Figure 2 , the oil recovery factor prediction model about the first parameter is constructed.
[0131] Based on the aggregation and dispersion degree of the data points in Figure 2 , the data points in the figure are divided into three categories, and the oil recovery factor prediction models corresponding to the three categories of data points are established, and the probabilities of the oil recovery factor prediction models under different categories are obtained. The probability here is the proportion of the number of data points in each category to the total number of data points.
[0132] Therefore, the expression of the oil recovery factor prediction model of the fracture and fracture-cave type reservoir and the corresponding probability are as follows:
[0133]
[0134] Among them, the first category corresponds to the gray data points in Figure 2 , the second category corresponds to the green data points in Figure 2 , and the third category corresponds to the red data points in Figure 2 .
[0135] (6) For the determined pore type reservoir, the nuclear magnetic effective porosity φ, the nuclear magnetic permeability K and the oil saturation S o are preferred as sensitive parameters to calculate the second parameter SRQI. Referring to Figure 3 , first, the 62 fluid production contributing intervals obtained in the fourth step are preliminarily selected, for example, according to whether the formation flowing pressure of the fluid production contributing interval is stable as the preliminary selection basis, that is, the fluid production contributing intervals with stable formation flowing pressure are preliminarily selected from the 62 fluid production contributing intervals, and the preliminarily selected fluid production contributing intervals are shown in Figure 3 , including the green circular data points and the gray triangular data points in the figure. Then, for the preliminarily selected fluid production contributing intervals, the final fluid production contributing intervals for constructing the oil recovery factor are selected again according to the relationship between the nuclear magnetic permeability as the sensitive parameter and the corresponding reservoir thickness. The specific method is to select the fluid production contributing interval with reservoir thickness greater than 2m and nuclear magnetic permeability greater than 10mD as the final fluid production contributing interval for constructing the oil recovery factor, that is, the green circular data shown in Figure 3 . The purpose of this is to improve the accuracy of the oil recovery factor prediction model.
[0136] The second parameter is plotted against the oil index, as shown in Figure 4 According to the data points in the graph, the data points are divided into three categories, and the oil index prediction model corresponding to each category is established, and the probability of the oil index prediction model in each category is obtained. The probability is the ratio of the number of data points in each category to the total number of data points.
[0137] Therefore, the expression of the oil index prediction model of the pore type reservoir and the corresponding probability are as follows:
[0138] logPI = 10.621 * ln (logSRQI) - 8.4689, probability 16.7% (first category);
[0139] logPI = 2.0933 * ln (logSRQI) - 0.3762, probability 66.6% (second category);
[0140] logPI = 32.733 * ln (logSRQI) - 11.046, probability 16.7% (third category);
[0141] In the above formula, the first category corresponds to the blue data points in the graph, the second category corresponds to the green data points in the graph, and the third category corresponds to the red data points in the graph. Figure 4
[0142] (7) According to the preset reservoir classification standard, the reservoir classification of the research layer system in the research well in the development stage or the evaluation stage is carried out. The reservoir classification information of a research well in the oilfield is shown in Figure 5 Figure 5 The column chart of the research well is shown in the figure, and the first column from the right is the reservoir classification according to the reservoir classification standard.
[0143] The above steps (1)-(7) form a reservoir classification constrained by the dynamic data of the PLT production logging.
[0144] (8) For each research well, select the corresponding oil reservoir comprehensive oil index formula to calculate the productivity of each research well. For different reservoir classification sections, select the corresponding oil index prediction model, and then determine the predicted oil index according to the oil index prediction model. The oil index is a parameter, and then the oil reservoir comprehensive oil index, i.e. the productivity of the research well, is calculated. In the embodiment of the present application, different oil index prediction models with different probabilities are selected under different reservoir spaces, which are selected according to experience, and the present application is not limited.
[0145] The calculated comprehensive oil recovery index of the research well is 5.88-11.2 bbl / d / psi. The oil recovery index of the research well tested is 8.2-11.9 bbl / d / psi, which is very close to the result obtained by applying the prediction method provided by the embodiment of the application.
[0146] The productivity prediction method for dynamic data-constrained reservoir classification provided by the embodiment of the application is used to evaluate the reservoir productivity of the oilfield in the example. After verification of multiple oil conclusions, it is found that the productivity prediction method has high discrimination accuracy, good applicability and effectiveness, and is an effective means for productivity prediction and evaluation in oilfields without DST testing wells. The method can be widely promoted and applied to other working conditions.
[0147] Based on the same inventive concept, the embodiment of the application also provides a productivity prediction device for dynamic data-constrained reservoir classification. Since the principle of the problem solved by the device is similar to the aforementioned productivity prediction method for dynamic data-constrained reservoir classification, the implementation of the device can be referred to the implementation of the aforementioned method, and the repeated parts will not be described again.
[0148] The embodiment of the application provides a productivity prediction device for dynamic data-constrained reservoir classification, as shown in Figure 6 , which comprises:
[0149] The fluid production contribution interval division module 61 is used to divide the PLT production logging test interval into multiple fluid production contribution intervals according to the analysis results of the dynamic data of multiple wells subjected to PLT production logging.
[0150] The oil recovery index determination module 62 is used to determine the oil recovery index corresponding to each fluid production contribution interval.
[0151] The reservoir space type determination module 63 is used to determine the reservoir space type of each fluid production contribution interval.
[0152] The prediction model construction module 64 is used to construct an oil recovery index prediction model of the reservoir with different reservoir space types.
[0153] The reservoir classification determination module 65 is used to determine the reservoir classification of the research layer series in the research well.
[0154] The productivity determination module 66 is used to determine the reservoir space type in different reservoir classification intervals and the oil recovery index prediction model corresponding to different reservoir classification intervals; determine the predicted oil recovery index of different reservoir classification intervals according to the oil recovery index prediction model; and determine the productivity of the research well according to the reservoir classification and the predicted oil recovery index.
[0155] The embodiment of the present application provides a kind of computing device, comprising: memory, processor and computer program stored in memory and can be run on processor, when processor executes program, it realizes the productivity prediction method of dynamic data constraint reservoir classification as described above.
[0156] The embodiment of the present application provides a kind of computer readable storage medium, computer readable storage medium stores computer program, when computer program is executed by processor, it realizes the productivity prediction method of dynamic data constraint reservoir classification as described above.
[0157] The embodiment of the present application provides a kind of computer program product, computer program product includes computer program, when computer program is executed by processor, it realizes the productivity prediction method of dynamic data constraint reservoir classification as described above.
[0158] Obviously, those skilled in the art can make various changes to the present application without departing from the spirit and scope of the present application. Thus, if these modifications of the present application belong to the scope of the claims of the present application and their equivalent technologies, the present application also intends to include these changes.
Claims
1. A method for predicting the productive capacity of reservoirs based on dynamic data-constrained reservoir classification, characterized in that, include: Based on the analysis results of dynamic data from multiple wells that have undergone PLT production logging, the PLT production logging test sections are divided into multiple fluid production contribution sections. Determine the oil recovery index corresponding to the fluid-producing contribution layer; The reservoir storage space type of each of the fluid-contributing sections is determined; Construct a mi-oil recovery index prediction model for reservoirs with different reservoir space types; Determine the reservoir classification of the study formation in the study well; Determine the reservoir space type in different reservoir classification segments, and the corresponding oil recovery index prediction model for the different reservoir classification segments; Based on the aforementioned oil recovery index prediction model, the predicted oil recovery index for the different reservoir classification segments is determined; The production capacity of the study well is determined based on the reservoir classification and the predicted oil recovery index.
2. The method as described in claim 1, characterized in that, The production capacity of the study well is determined based on the reservoir comprehensive oil recovery index J0 of the study well; The composite meter oil recovery index of the study well is used to characterize the production capacity of the study well; The comprehensive oil recovery index J0 of the study well is calculated according to the following formula: Where: J0 is the reservoir comprehensive oil recovery index; i represents the reservoir classification segment number; n is the total number of reservoir classification segments; h i The thickness of the i-th reservoir classification segment; q i The predicted oil recovery index is for the i-th reservoir classification segment.
3. The method as described in claim 1, characterized in that, The oil recovery index of the producing zone is calculated according to the following formula: Where: PI is the oil extraction index; Q o The production rate in the fluid-producing section is represented by H, which is the effective thickness of the oil layer; P is the production rate in the fluid-producing section. e Formation pressure; P f For flow pressure.
4. The method as described in claim 1, characterized in that, The method for determining the storage space type is as follows: Based on the core, wall core, thin section, mercury injection data and completion interpretation results of the PLT production logging test section, as well as the obtained logging curves of the PLT production logging test section, the fracture, pore development and pore throat structure characteristics of the test section are determined. Based on the fluid production contribution capacity of the multiple fluid-producing contributing segments in the test layer, as well as the development of fractures, pores, and pore throat structure characteristics of the test layer, the reservoir space type that affects the fluid production contribution capacity of the reservoir is determined.
5. The method as described in claim 4, characterized in that, The storage space types include: crack, fissure, and pore.
6. The method as described in claim 5, characterized in that, For the fractured and fracture-vuggy reservoirs, a corresponding enhanced oil recovery index (EOR) prediction model is constructed, including: Identify the sensitive parameters that affect fractured and fracture-vuggy reservoirs; Based on the aforementioned sensitive parameters, a first parameter RQI affecting the reservoir quality of fractured and fracture-vuggy reservoirs is constructed. k ; Establish a cross plot of the first parameter and the corresponding oil extraction index; Based on the intersection diagram, a predictive model for the oil recovery index of fractured and fractured-vuggy reservoirs is established.
7. The method as described in claim 6, characterized in that, The sensitive parameters affecting fractured and fractured-vuggy reservoirs include nuclear magnetic resonance effective porosity and nuclear magnetic resonance permeability. The first parameter RQI that influences reservoir quality, including fracture and fracture-vuggy reservoirs, is described. k ,include: The quality factor and breakthrough coefficient are determined based on the NMR effective porosity and NMR permeability. The first parameter is determined based on the quality factor and the surge coefficient; The first parameter is calculated according to the following formula: RQI k =RQI×T k ; Among them: RQI k The first parameter is RQI, which is the reservoir quality factor; T is the reservoir quality factor. k This is the surge coefficient.
8. The method as described in claim 5, characterized in that, For the porous reservoirs, a corresponding oil recovery index (ERI) prediction model is constructed, including: Identify the sensitive parameters that affect porous reservoirs; Based on the aforementioned sensitive parameters, a second parameter SRQI that affects the quality of porous reservoirs is constructed; Establish a cross plot of the second parameter and the corresponding oil extraction index; Based on the cross plot, a predictive model for the oil recovery index of porous reservoirs is established.
9. The method as described in claim 8, characterized in that, The sensitive parameters affecting the porosity of reservoirs include nuclear magnetic resonance effective porosity, nuclear magnetic resonance permeability, and oil saturation. The second parameter, SRQI, is calculated according to the following formula: Wherein: S o φ represents oil saturation; φ represents nuclear magnetic resonance porosity; RQI is the reservoir quality factor, which is determined based on nuclear magnetic resonance porosity and nuclear magnetic permeability.
10. A dynamic data-constrained reservoir classification-based productivity prediction device, characterized in that, include: The fluid production contribution segment division module is used to divide the PLT production logging test segment into multiple fluid production contribution segments based on the analysis results of dynamic data from multiple wells that have undergone PLT production logging. The oil recovery index determination module is used to determine the oil recovery index corresponding to the fluid-producing contributing layer. The reservoir space type determination module is used to determine the reservoir space type of the liquid-producing contributing layer, respectively. The prediction model building module is used to build a mi-oil recovery index prediction model for reservoirs with different reservoir space types. The reservoir classification determination module is used to determine the reservoir classification of the study formation in the study well; The production capacity determination module is used to determine the reservoir space type in different reservoir classification segments and the corresponding oil recovery index prediction model for the different reservoir classification segments; determine the predicted oil recovery index for the different reservoir classification segments based on the oil recovery index prediction model; and determine the production capacity of the study well based on the reservoir classification and the predicted oil recovery index.
11. A computing device, characterized in that, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the program, implements the dynamic data-constrained reservoir classification method for predicting production capacity as described in any one of claims 1-9.
12. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the dynamic data-constrained reservoir classification production prediction method according to any one of claims 1-9.
13. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the dynamic data-constrained reservoir classification production prediction method according to any one of claims 1-9.