Gas reservoir productivity prediction method and device and electronic equipment
By establishing a reservoir capacity prediction model and determining fracture parameters using electrical imaging and conventional well logging data, the problem of low unimpeded flow prediction accuracy of fracture-type dense clastic rock gas reservoirs is solved, and high-precision capacity prediction and low-cost exploration and development are achieved.
Patent Information
- Application Number
- CN202311773679.3
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2023-12-21
- Publication Date
- 2025-07-22
AI Technical Summary
The existing gas well capacity calculation method has low prediction accuracy for crack-type dense clastic rock gas reservoirs, especially in western Sichuan, Xu's second-fire reservoir, which cannot accurately predict unimpeded flow.
By establishing a reservoir capacity prediction model, the reservoir fracture parameters and unhindered flow of the target area have been used to measure the reservoir fracture parameters and unhindered flow of the target well, combined with the electro-imaging logging data and conventional well logging data, the reservoir fracture parameters and unhindered flow of the target well, including the number of electrical imaging fracture development lines and the fracture development thickness, are constructed.
It improves the prediction accuracy and reliability of unhindered flow of crack-type dense clastic rock gas reservoirs, reduces the cost of identification of fracture reservoirs, and improves prediction accuracy and exploration and development efficiency.
Smart Images
Figure CN120355523A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of production capacity prediction, and particularly to a gas reservoir production capacity prediction method, device and electronic equipment. Background Art
[0002] At present, the production capacity calculation methods of gas wells mainly include formula calculation method and empirical model method.
[0003] The formula calculation method mainly establishes a steady-state or unsteady-state production capacity equation through the Darcy equation of radial flow, and then directly substitutes reservoir parameters for calculation. This method is affected by parameters such as skin factor that are difficult to determine, and the accuracy is relatively low.
[0004] The empirical model method is mostly based on the relationship between reservoir parameters and open flow potential, and is related to reservoir thickness and matrix porosity. Since the relationship between fracture parameters and reservoir production capacity is not analyzed, there are obvious limitations in production capacity prediction. When using the empirical model method to predict the production capacity of fractured reservoirs (such as the Xujiahe fractured reservoir in Western Sichuan), the prediction accuracy is relatively low.
[0005] Therefore, there is an urgent need for a new production capacity prediction method to accurately predict the production capacity of fractured tight clastic gas reservoirs. Summary of the Invention
[0006] In view of the above problems, the present invention provides a gas reservoir production capacity prediction method, device and electronic equipment, which realizes the accurate prediction of the production capacity of fractured tight clastic gas reservoirs.
[0007] The gas reservoir production capacity prediction method provided by the present invention includes:
[0008] Establishing a reservoir production capacity prediction model according to the reservoir fracture parameters and reservoir open flow potential of each gas well that has completed single-well testing in the target area at the target horizon;
[0009] Determining the reservoir fracture parameters of the target well at the target horizon according to the logging data of the target well in the target area;
[0010] Determining the reservoir open flow potential of the target well at the target horizon according to the reservoir production capacity prediction model and the reservoir fracture parameters of the target well at the target horizon.
[0011] Further, the reservoir fracture parameters include the number of reservoir electroimaging fracture development. Determining the reservoir fracture parameters of the target well at the target horizon according to the logging data of the target well in the target area includes:
[0012] Determining the number of reservoir electroimaging fracture development of the target well at the target horizon according to the electroimaging logging data of the target well.
[0013] Further, the reservoir productivity prediction model includes a first reservoir productivity prediction model. Based on the reservoir fracture parameters and the open flow potential of each gas well that has completed single-well testing in the target area at the target horizon, a reservoir productivity prediction model is established, including:
[0014] Based on the number of fractures developed in the reservoir imaged by electric logging and the open flow potential of each gas well that has completed single-well testing in the target area at the target horizon, a first reservoir productivity prediction model is established.
[0015] Further, based on the reservoir productivity prediction model and the reservoir fracture parameters of the target well at the target horizon, the open flow potential of the target well at the target horizon is determined, including:
[0016] Based on the number of fractures developed in the reservoir imaged by electric logging of the target well at the target horizon and the first reservoir productivity prediction model, the open flow potential of the target well at the target horizon is determined.
[0017] Further, the reservoir fracture parameters include the thickness of reservoir fracture development. Based on the logging data of the target well in the target area, the reservoir fracture parameters of the target well at the target horizon are determined, including:
[0018] Based on the deep lateral resistivity logging data and the natural gamma logging data of the target well, the thickness of reservoir fracture development of the target well at the target horizon is determined.
[0019] Further, the reservoir productivity prediction model includes a second reservoir productivity prediction model; based on the reservoir fracture parameters and the open flow potential of each gas well that has completed single-well testing in the target area at the target horizon, a reservoir productivity prediction model is established, including:
[0020] Based on the thickness of reservoir fracture development and the open flow potential of each gas well that has completed single-well testing in the target area at the target horizon, a second reservoir productivity prediction model is established.
[0021] Further, based on the reservoir productivity prediction model and the reservoir fracture parameters of the target well at the target horizon, the open flow potential of the target well at the target horizon is determined, including:
[0022] Based on the thickness of reservoir fracture development of the target well at the target horizon and the second reservoir productivity prediction model, the open flow potential of the target well at the target horizon is determined.
[0023] Further, based on the deep lateral resistivity logging data and the natural gamma logging data of the target well, the equivalent thickness of reservoir fracture development of the target well at the target horizon is determined, including:
[0024] Take the section corresponding to the target horizon in the target well as the pre-test section;
[0025] Respectively determine the first ratio between the resistivity and the natural gamma corresponding to the starting depth of each pre-test section;
[0026] Determine the second ratio between the resistivity and the natural gamma corresponding to the termination depth of each pre-test interval respectively;
[0027] Take the difference between the second ratio and the first ratio corresponding to each pre-test interval as the fracture development thickness corresponding to each pre-test interval;
[0028] Take the sum of the fracture development thicknesses corresponding to all the pre-test intervals as the reservoir fracture development thickness of the target well in the target formation.
[0029] The present invention also provides a gas reservoir productivity prediction device, which includes:
[0030] A model construction module, configured to establish a reservoir productivity prediction model according to the reservoir fracture parameters and the open flow potential of each gas well that has completed single-well testing in the target formation in the target area;
[0031] A parameter determination module, configured to determine the reservoir fracture parameters of the target well in the target formation according to the logging data of the target well in the target area;
[0032] A productivity prediction module, configured to determine the open flow potential of the target well in the target formation according to the reservoir productivity prediction model and the reservoir fracture parameters of the target well in the target formation.
[0033] The present invention also provides a computer-readable storage medium, and a computer program stored in the computer-readable storage medium, when executed by one or more processors, implements the steps of the above method.
[0034] The present invention also provides an electronic device, including a memory and one or more processors, and a computer program is stored on the memory, and when the computer program is executed by one or more processors, the steps of the above method are executed.
[0035] The gas reservoir productivity prediction method, device and electronic device provided by the present invention have at least the following beneficial effects:
[0036] (1) Through the research on the logging productivity prediction method for fractured tight clastic gas reservoirs, the open flow potential of fractured reservoirs can be predicted more accurately, and the problem of predicting the open flow potential of fractured reservoirs is solved.
[0037] (2) When establishing the reservoir productivity prediction model, the influence of reservoir fracture parameters on the open flow potential is fully considered. When using this model for productivity prediction, the accuracy and reliability of predicting the open flow potential of fractured tight clastic reservoirs are improved, and the prediction accuracy is improved.
[0038] (3) By effectively calculating the equivalent reservoir fracture development thickness using the deep lateral resistivity curve (RD) and natural gamma curve (GR) in conventional logging curves, the cost of fracture reservoir identification is reduced, and the exploration and development efficiency is improved.
[0039] (4) Using the gas reservoir productivity prediction method provided by the present invention can provide a scientific basis for the rational development and utilization of fractured tight clastic rock gas reservoirs, guide the actual gas production engineering, and has important application value. BRIEF DESCRIPTION OF THE DRAWINGS
[0040] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the following described drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.
[0041] Figure 1 It is a flowchart of the gas reservoir productivity prediction method provided in an embodiment of the present invention;
[0042] Figure 2 It is a schematic diagram of the relationship between the number of fractures developed in the reservoir electrical imaging and the open flow potential of the reservoir;
[0043] Figure 3 It is a schematic diagram of the logging data of Well A in the CX area;
[0044] Figure 4 It is a schematic diagram of the relationship between the fracture development thickness of the reservoir and the open flow potential of the reservoir;
[0045] Figure 5 It is a schematic diagram of the comparison between the predicted result and the actual result of the open flow potential of Well A in the CX area;
[0046] Figure 6 It is a schematic diagram of the logging data of Well B in the CX area;
[0047] Figure 7 It is a schematic diagram of the comparison between the predicted result and the actual result of the open flow potential of Well B in the CX area;
[0048] Figure 8 It is a schematic diagram of the structure of the gas reservoir productivity prediction device provided in an embodiment of the present invention;
[0049] Figure 9 It is a schematic diagram of the structure of the electronic device provided in an embodiment of the present invention;
[0050] Reference Signs:
[0051] Figure 8Chinese: 801 - Model construction module, 802 - Parameter determination module, 803 - Production capacity prediction module
[0052] Figure 9 Chinese: 900 - Electronic device, 901 - Processor, 902 - Communication bus, 903 - User interface, 904 - External communication interface, 905 - Memory. Detailed implementation manners
[0053] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention. Those skilled in the art can understand that the terms "first", "second", etc. in the embodiments of the present invention are only used to distinguish different steps, devices or modules, etc., without representing any specific technical meaning and without indicating their inevitable logical order.
[0054] The Xinchang Xujiahe gas reservoir in Western Sichuan has the characteristics of "two lows and one high", that is, extra-low porosity, the average porosity of the effective reservoir is only 3.75%, extra-low permeability, the average permeability is only 0.07 mD, and the rock fracture pressure is high, concentrated in 100 - 165 Mpa. The reservoir rocks in the Xujiahe Formation in Western Sichuan are well-developed in fractures. The fractures are the channels for communication between pores and act as the throats. The development of fractures will greatly improve the connectivity of the reservoir and the seepage conditions of fluids. The development of fractures is the key to the high and stable production of gas wells.
[0055] The present invention solves the problem of predicting the production capacity of fractured tight clastic gas reservoirs by obtaining reservoir fracture parameters and establishing a reservoir production capacity prediction model according to the relationship between reservoir fracture parameters and the open flow potential of the reservoir. It is especially applicable to the Xujiahe 2 fracture body reservoir in Western Sichuan, and can accurately predict the open flow potential of a single well in the difficult-to-produce reserves of Xujiahe 2 in Western Sichuan, which is of great significance for the production capacity construction and economic development of the difficult-to-produce reserves in the Xujiahe 2 gas reservoir in Western Sichuan.
[0056] In an embodiment of the present invention, a method for predicting the production capacity of a gas reservoir is provided, as Figure 1 shown, and the method includes the following steps:
[0057] Step S101: Establish a reservoir production capacity prediction model according to the reservoir fracture parameters and the open flow potential of each gas well in the target layer of the gas wells that have completed single-well tests in the target area.
[0058] Specifically, in step S101, the specific process of single-well testing includes: (1) Conducting single-well logging to obtain the logging interpretation results; (2) Conducting productivity testing to obtain the single-well productivity (open-flow potential of the reservoir) at the target horizon. Therefore, for the gas wells that have completed single-well testing, the reservoir fracture parameters at the target horizon can be obtained based on the logging interpretation results, and the obtained single-well productivity can be used as the open-flow potential of the reservoir at the target horizon. The target horizon refers to the fractured reservoir for which productivity prediction is to be carried out. More specifically, in one implementation, the target horizon can be the Xujiahe Formation fracture-block reservoir in western Sichuan.
[0059] Step S102: Determine the reservoir fracture parameters of the target well at the target horizon according to the logging data of the target well in the target area.
[0060] Step S103: Determine the open-flow potential of the target well at the target horizon according to the reservoir productivity prediction model and the reservoir fracture parameters of the target well at the target horizon.
[0061] Optionally, the reservoir fracture parameters include the number of fractures developed in the reservoir as shown by the electrical image logging. Step S102 includes: determining the number of fractures developed in the reservoir as shown by the electrical image logging of the target well at the target horizon according to the electrical image logging data of the target well. Further, the reservoir productivity prediction model includes the first reservoir productivity prediction model. Step S101 includes: establishing the first reservoir productivity prediction model according to the number of fractures developed in the reservoir as shown by the electrical image logging and the open-flow potential of each gas well that has completed single-well testing at the target horizon.
[0062] Specifically, in one implementation, establishing the first reservoir productivity prediction model according to the number of fractures developed in the reservoir as shown by the electrical image logging and the open-flow potential of each gas well that has completed single-well testing at the target horizon may include:
[0063] According to the number of fractures developed in the reservoir as shown by the electrical image logging and the open-flow potential of each gas well that has completed single-well testing at the target horizon, draw a relationship diagram of the number of fractures developed in the reservoir as shown by the electrical image logging and the open-flow potential (see Figure 2 , Figure 2 the curve in which is the relationship curve of the number of fractures developed in the reservoir as shown by the electrical image logging and the open-flow potential obtained by fitting);
[0064] Based on the relationship diagram of the number of fractures developed in the reservoir as shown by the electrical image logging and the open-flow potential, fit the relationship formula between the number of fractures developed in the electrical image logging and the open-flow potential (this relationship formula is the first reservoir productivity prediction model).
[0065] Among them, based on Figure 2 , the relationship formula between the number of fractures developed in the electrical image logging and the open-flow potential obtained by fitting is:
[0066]
[0067] Among them, QAOF is the open flow potential of the reservoir, N f is the number of fractures developed in the reservoir imaged by electric logging in the target well.
[0068] Furthermore, step S103 includes: determining the open flow potential of the reservoir in the target layer of the target well according to the number of fractures developed in the reservoir imaged by electric logging in the target layer of the target well and the first reservoir productivity prediction model.
[0069] That is, in the case where the electric imaging logging is included in the single well logging project and the first reservoir productivity prediction model has been constructed, the number of fractures developed in the reservoir imaged by electric logging in the target layer can be directly obtained according to the electric imaging logging data of the target well, and the open flow potential of the single well can be predicted based on the productivity prediction relationship.
[0070] See Figure 3 , Figure 3 in the corresponding logging project, electric imaging logging is included, and among them, the number of fractures developed in the reservoir imaged by electric logging can be determined according to Figure 3 the fracture azimuth map in (each tadpole-shaped trace in the azimuth map represents a fracture). Specifically, the number of fractures developed in the reservoir imaged by electric logging is the number of tadpole-shaped traces in the layer segment corresponding to the target layer in the fracture azimuth map.
[0071] Optionally, the reservoir fracture parameters include the developed thickness of the reservoir fractures. Step S102 includes: determining the developed thickness of the reservoir fractures in the target layer of the target well according to the deep lateral resistivity logging data and the natural gamma logging data of the target well. Further, the reservoir productivity prediction model includes the second reservoir productivity prediction model. Step S101 includes: establishing the second reservoir productivity prediction model according to the developed thickness of the reservoir fractures and the open flow potential of the reservoir in the target layer of each gas well that has completed the single well test.
[0072] Specifically, in one implementation, establishing the second reservoir productivity prediction model according to the developed thickness of the reservoir fractures and the open flow potential of the reservoir in the target layer of each gas well that has completed the single well test may include:
[0073] Drawing a relationship diagram of the developed thickness of the reservoir fractures and the open flow potential of the reservoir according to the developed thickness of the reservoir fractures and the open flow potential of the reservoir in the target layer of each gas well that has completed the single well test (see Figure 4 , Figure 4 the curve in is the relationship curve of the developed thickness of the reservoir fractures and the open flow potential of the reservoir obtained by fitting);
[0074] Based on the relationship diagram of the developed thickness of the reservoir fractures and the open flow potential of the reservoir, fitting out the relationship formula between the developed thickness of the fractures and the open flow potential (this relationship formula is the second reservoir productivity prediction model).
[0075] Among them, based on Figure 4, the relationship between the fracture development thickness and the open flow potential obtained by fitting is as follows:
[0076]
[0077] Among them, Q AOF is the open flow potential of the reservoir, and H f is the fracture development thickness of the reservoir.
[0078] Furthermore, step S103 includes: determining the open flow potential of the target well in the target formation according to the equivalent fracture development thickness of the target well in the target formation and the second reservoir productivity prediction model.
[0079] That is, in the case of no resistivity imaging logging in the single-well logging project and the second reservoir productivity prediction model has been constructed, the equivalent fracture development thickness of the reservoir can be calculated using the deep lateral resistivity curve (RD) and the natural gamma curve (GR) in the conventional logging curves, and then the productivity prediction can be carried out through the second reservoir productivity prediction model.
[0080] The performance characteristics of fractures on the natural gamma curve (GR) are as follows: when there are no highly radioactive elements in the open fractures, the GR measurement value is relatively low; when the filled fractures are filled with shale, the GR measurement value fluctuates; when uranium minerals separated by groundwater activities adhere to the fracture walls, the GR shows an abnormally high value. Generally, when the fractures are in good condition, the natural gamma is a low value.
[0081] The performance characteristics of fractures on the dual laterolog curves are as follows: in the dual laterolog response, in the fracture development section, the resistivity value is significantly lower than the high resistivity background of the dense formation, and the curve shape is sharp. When the aperture increases, the difference in the amplitudes of the deep and shallow lateral resistivity caused by fractures increases. The fracture scale of the network fractures is relatively large, generally extending far in both the longitudinal and transverse directions. The dual laterolog decreases significantly on the high resistivity background and has a certain thickness, unlike the spiky characteristics shown by the low-angle fractures.
[0082] Accordingly, by using the characteristics that fractures cause no change in natural gamma and a significant decrease in resistivity, the ratio of resistivity to natural gamma is used for fracture identification, the equivalent fracture development thickness of the reservoir is calculated, and then the open flow potential of a single well is predicted.
[0083] In this embodiment, the equivalent fracture development thickness of the reservoir is effectively calculated using the deep lateral resistivity curve (RD) and the natural gamma curve (GR) in the conventional logging curves, reducing the cost of fracture reservoir identification and improving the exploration and development efficiency.
[0084] Specifically, determining the fracture development thickness of the reservoir of the target well in the target formation according to the deep lateral resistivity logging data and the natural gamma logging data of the target well includes:
[0085] Take the interval corresponding to the target horizon in the target well as the pre-test interval;
[0086] Determine the first ratio between the resistivity and the natural gamma corresponding to the starting depth of each pre-test interval respectively;
[0087] Determine the second ratio between the resistivity and the natural gamma corresponding to the ending depth of each pre-test interval respectively;
[0088] Take the difference between the second ratio and the first ratio corresponding to each pre-test interval as the fracture development thickness corresponding to each pre-test interval;
[0089] Take the sum of the fracture development thicknesses corresponding to all the pre-test intervals as the reservoir fracture development thickness of the target well at the target horizon.
[0090] It can be understood that the pre-test interval represents the position (depth range) of the target horizon in the target well. In practical applications, the resistivity corresponding to the starting depth of the prediction interval and the resistivity corresponding to the ending depth of the prediction interval can be obtained from the deep lateral resistivity curve; the natural gamma corresponding to the starting depth of the prediction interval and the natural gamma corresponding to the ending depth of the prediction interval can be obtained from the natural gamma curve, and then the first ratio and the second ratio are determined to obtain the reservoir fracture development thickness.
[0091] It should be noted that in practical applications, technicians can choose to pre-construct the first reservoir productivity prediction model and / or the second reservoir productivity prediction model.
[0092] Optionally, in one implementation, the gas reservoir productivity prediction method further includes:
[0093] Step S104: Verify the effect of the logging productivity prediction method for fractured tight clastic gas reservoirs.
[0094] In step S104, the prediction result can be compared and analyzed with the actual test open flow rate to verify the accuracy of the predicted open flow rate by the gas reservoir productivity prediction method.
[0095] For the gas reservoir productivity prediction method provided in this embodiment, when establishing the reservoir productivity prediction model, the influence of reservoir fracture parameters on the open flow rate is fully considered. When using this model for productivity prediction, the accuracy and reliability of the predicted open flow rate of fractured tight clastic reservoirs are improved, the prediction accuracy is improved, the problem of predicting the open flow rate of fractured reservoirs is solved, and the principle is simple and there is no obvious inapplicable problem, which is easy to be popularized on a large scale; in addition, by effectively calculating the equivalent reservoir fracture development thickness using the deep lateral resistivity curve (RD) and the natural gamma curve (GR) in the conventional logging curves, the cost of fracture reservoir identification is reduced, and the exploration and development efficiency are improved.
[0096] In yet another embodiment of the present invention, a specific example of using the gas reservoir productivity prediction method of the present invention for productivity prediction is provided.
[0097] This embodiment provides a logging productivity prediction method for fractured tight clastic rock gas reservoirs, which is applied to the CX work area in the Sichuan Basin. The specific details are as follows:
[0098] In the CX area, obtain the number of electro-imaging fractures developed and the thickness of reservoir fractures developed in each gas well that has completed single-well testing. Draw a relationship diagram between the number of electro-imaging fractures developed in the reservoir and the open flow potential of the reservoir (see Figure 2 ), and a relationship diagram between the thickness of reservoir fractures developed and the open flow potential (see Figure 4 ).
[0099] Based on the relationship diagram between reservoir parameters and open flow potential, fit the relationship formula between the number of electro-imaging fractures developed and the open flow potential
[0100]
[0101] Fit the relationship formula between the thickness of reservoir fractures developed and the open flow potential
[0102]
[0103] Furthermore, establish a reservoir productivity prediction model for the CX area.
[0104] Take Well A in the CX area as the target well. In Well A in the CX area, electro-imaging logging measurements were carried out. Among them, the intervals corresponding to the target horizons in Well A are 4602m to 4681m, 4733m to 4773m, and 4799m to 4826m. According to the interpretation results of electro-imaging high-conductivity fractures, extract the number of electro-imaging fractures developed in the reservoir within the pretest intervals (4602m to 4681m, 4733m to 4773m, 4799m to 4826m) as 56 (see Figure 3 ). According to the relationship formula between the number of electro-imaging fractures developed and the open flow potential, predict the open flow potential of Well A to be 351,200 m³ / day. Compare the prediction result with the actual measured open flow potential to verify the accuracy of the logging productivity prediction method for fractured tight clastic rock gas reservoirs in predicting the open flow potential. The predicted open flow potential of Well A is 351,200 m³ / day, and the measured open flow potential is 288,000 m³ / day. The prediction result is relatively consistent with the actual result (see Figure 5 , Figure 5 . The red dot in
[0105] Analyze the accuracy of the open flow prediction model with reference to the open flow test results of Well A. Take Well B in the CX area as the target well. In Well B, electrical image logging measurements were not carried out. Therefore, the equivalent reservoir fracture development thickness can be calculated using the deep lateral resistivity curve (RD) and natural gamma curve (GR) in the conventional logging curves, and then the open flow of a single well can be predicted. Among them, the intervals corresponding to the target horizons in Well B are from 4629 m to 4715 m and from 4755 m to 4855 m. According to the calculation results of the equivalent fracture development thickness, the equivalent reservoir fracture development thickness within the pretest intervals (from 4629 m to 4715 m and from 4755 m to 4855 m) in Well B is 41.2 m (see Figure 6 ). According to the relationship between the fracture development thickness and the open flow within the reservoir section, the open flow of Well B is calculated to be 617,000 m³ / day, and the measured open flow is 530,000 m³ / day. The prediction result is in good agreement with the actual result (see Figure 7 , Figure 7 . The red dot in
[0106] is the actual result of Well B, and the points on the curve are the prediction results obtained by using the method provided by the present invention).
[0106] To more clearly illustrate the advantages of the logging productivity prediction method for fractured tight clastic gas reservoirs provided by the present invention compared with the productivity prediction methods in the prior art, a comparative example is given below:
[0107] This comparative example provides an existing productivity prediction method applied to the productivity prediction of Well A in the CX area. The specific details are as follows:
[0108] The existing evaluation method only considers the effective thickness, effective porosity, permeability, and gas saturation of the reservoir, and does not consider the influence of the number of reservoir fractures or the influence of the fracture development thickness of the reservoir. In the CX work area, the average porosity of the reservoir is 3.75% and the average permeability is only 0.07 mD, which is a very low porosity and very low permeability reservoir. In Well A, the porosity in the interval of 4733 - 4773 m is mostly below 3%, and the gas logging anomaly value in the logging is low. Without considering the influence of the fracture development situation, this interval will be judged as an ultra-dense layer in the actual test, making it difficult to carry out fracturing construction and transformation, which will lead to the abandonment of this interval, thereby reducing the actual productivity and making the productivity prediction lower than the actual test result (see Figure 5 , Figure 5 . The blue dots in
[0109] are the prediction results of productivity prediction using the prior art, and the red dots are the actual test results).
[0109] In another embodiment of the present invention, as shown in Figure 8 , a gas reservoir productivity prediction device is also provided. The device includes:
[0110] The model construction module 801 is used to establish a reservoir productivity prediction model according to the reservoir fracture parameters and the open flow potential of each gas well that has completed single-well testing in the target layer of the target area.
[0111] The parameter determination module 802 is used to determine the reservoir fracture parameters of the target well in the target layer according to the logging data of the target well in the target area.
[0112] The productivity prediction module 803 is used to determine the open flow potential of the target well in the target layer according to the reservoir productivity prediction model and the reservoir fracture parameters of the target well in the target layer.
[0113] Optionally, the reservoir fracture parameters include the number of developed fractures in the reservoir imaged by electricity. The parameter determination module 802 determines the reservoir fracture parameters of the target well in the target layer according to the logging data of the target well in the target area, including:
[0114] Determine the number of developed fractures in the reservoir imaged by electricity of the target well in the target layer according to the electrical imaging logging data of the target well.
[0115] Optionally, the reservoir productivity prediction model includes the first reservoir productivity prediction model. The model construction module 801 establishes a reservoir productivity prediction model according to the reservoir fracture parameters and the open flow potential of each gas well that has completed single-well testing in the target layer of the target area, including:
[0116] Establish the first reservoir productivity prediction model according to the number of developed fractures in the reservoir imaged by electricity and the open flow potential of each gas well that has completed single-well testing in the target layer.
[0117] Optionally, the productivity prediction module 803 determines the open flow potential of the target well in the target layer according to the reservoir productivity prediction model and the reservoir fracture parameters of the target well in the target layer, including:
[0118] Determine the open flow potential of the target well in the target layer according to the number of developed fractures in the reservoir imaged by electricity of the target well in the target layer and the first reservoir productivity prediction model.
[0119] Optionally, the reservoir fracture parameters include the developed thickness of the reservoir fractures. The parameter determination module 802 determines the reservoir fracture parameters of the target well in the target layer according to the logging data of the target well in the target area, including:
[0120] Determine the developed thickness of the reservoir fractures of the target well in the target layer according to the deep lateral resistivity logging data and the natural gamma logging data of the target well.
[0121] Optionally, the reservoir productivity prediction model includes a second reservoir productivity prediction model; the model construction module 801 establishes a reservoir productivity prediction model based on the reservoir fracture parameters and the open flow potential of each gas well with completed single-well testing in the target area at the target horizon, including:
[0122] Establish a second reservoir productivity prediction model based on the reservoir fracture development thickness and the open flow potential of each gas well with completed single-well testing in the target area at the target horizon.
[0123] Optionally, the productivity prediction module 803 determines the open flow potential of the target well at the target horizon according to the reservoir productivity prediction model and the reservoir fracture parameters of the target well at the target horizon, including:
[0124] Determine the open flow potential of the target well at the target horizon according to the reservoir fracture development thickness of the target well at the target horizon and the second reservoir productivity prediction model.
[0125] Optionally, according to the deep lateral resistivity logging data and the natural gamma logging data of the target well, determine the equivalent reservoir fracture development thickness of the target well at the target horizon, including:
[0126] Take the section corresponding to the target horizon in the target well as the prediction section; respectively determine the first ratio between the resistivity and the natural gamma corresponding to the starting depth of each prediction section;
[0127] Respectively determine the second ratio between the resistivity and the natural gamma corresponding to the ending depth of each prediction section;
[0128] Respectively take the difference between the second ratio and the first ratio corresponding to each prediction section as the fracture development thickness corresponding to each prediction section;
[0129] Take the sum of the fracture development thicknesses corresponding to all prediction sections as the reservoir fracture development thickness of the target well at the target horizon. In the gas reservoir productivity prediction device provided in this embodiment, when establishing the reservoir productivity prediction model, the influence of reservoir fracture parameters on the open flow potential is fully considered. When using this model for productivity prediction, the accuracy and reliability of the open flow potential prediction of fractured tight clastic rock reservoirs are improved, the prediction accuracy is improved, the problem of predicting the open flow potential of fractured reservoirs is solved, and the principle is simple and there is no obvious inapplicable problem, which is easy to be popularized on a large scale; in addition, by effectively calculating the equivalent reservoir fracture development thickness using the deep lateral resistivity curve (RD) and the natural gamma curve (GR) in the conventional logging curves, the cost of fracture reservoir identification is reduced, and the exploration and development efficiency are improved.
[0130] In yet another embodiment of the present invention, there is also provided a computer program product, which includes a computer program or instructions. When the computer program or instructions are executed by a processor, all or part of the steps of the method in the foregoing embodiments are implemented, and this embodiment will not be repeated here.
[0131] Further, the computer program product may include one or more computer-executable components configured to execute the embodiments when the program is running; the computer program product may also include a computer program tangibly embodied on a computer-readable medium, and the computer program includes program code for executing any method in the embodiments of the present disclosure. In such an embodiment, the computer program can be downloaded and installed from a network through a communication part, and / or installed from a removable medium.
[0132] In yet another embodiment of the present invention, there is also provided a computer-readable storage medium. When the computer program stored in the computer-readable storage medium is executed by one or more processors, all or part of the steps of the method in the foregoing embodiments are implemented, and this embodiment will not be repeated here.
[0133] In yet another embodiment of the present invention, there is also provided an electronic device Figure 9 which is a schematic structural diagram of the electronic device provided in the embodiments of the present application. As Figure 9 shown, the electronic device 900 includes: at least one processor 901, at least one communication bus 902, a user interface 903, at least one external communication interface 904, and a memory 905. Among them, the communication bus 902 is configured to implement connection communication between these components. Among them, the user interface 903 may include a display screen, and the external communication interface 904 may include a standard wired interface and a wireless interface. A computer program is stored on the memory 905, and the memory 905 and one or more processors 901 are communicatively connected to each other. When the computer program is executed by one or more processors, the processor 901 is configured to execute the computer program stored in the memory to implement all or part of the steps of the method in the foregoing embodiments, and this embodiment will not be repeated here.
[0134] The gas reservoir productivity prediction method, device and electronic device provided by the present invention fully consider the influence of reservoir fracture parameters on the open flow potential when establishing a reservoir productivity prediction model. When using this model for productivity prediction, the accuracy and reliability of the open flow potential prediction of fractured tight clastic rock reservoirs are improved, the prediction accuracy is improved, the problem of predicting the open flow potential of fractured reservoirs is solved, and the principle is simple and there is no obvious inapplicable problem, which is easy to be popularized on a large scale; in addition, by effectively calculating the equivalent reservoir fracture development thickness using the deep lateral resistivity curve (RD) and natural gamma curve (GR) in conventional logging curves, the cost of fracture reservoir identification is reduced, and the exploration and development efficiency are improved.
[0135] The terms and expressions used in the description of the present invention are for illustrative purposes only and are not intended to be limiting. Those skilled in the art should understand that various changes can be made to the details of the above-described embodiments without departing from the basic principles of the disclosed embodiments. Therefore, the scope of the present invention is determined only by the claims, and in the claims, unless otherwise specified, all terms should be understood in the broadest reasonable sense.
Claims
1. A gas reservoir productivity prediction method, characterized in that, The method includes: Establishing a reservoir productivity prediction model based on the reservoir fracture parameters and the open flow potential of each gas well with completed single-well testing in the target layer of the target area; Determining the reservoir fracture parameters of the target well in the target layer according to the logging data of the target well in the target area; Determining the open flow potential of the target well in the target layer according to the reservoir productivity prediction model and the reservoir fracture parameters of the target well in the target layer.
2. The gas reservoir productivity prediction method according to claim 1, wherein The reservoir fracture parameters include the number of developed fractures imaged by resistivity logging. The step of determining the reservoir fracture parameters of the target well in the target layer according to the logging data of the target well in the target area includes: Determining the number of developed fractures imaged by resistivity logging of the target well in the target layer according to the resistivity imaging logging data of the target well.
3. The gas reservoir productivity prediction method according to claim 2, characterized in that The reservoir productivity prediction model includes a first reservoir productivity prediction model. The step of establishing a reservoir productivity prediction model based on the reservoir fracture parameters and the open flow potential of each gas well with completed single-well testing in the target layer of the target area includes: Establishing the first reservoir productivity prediction model according to the number of developed fractures imaged by resistivity logging and the open flow potential of each gas well with completed single-well testing in the target layer.
4. The gas reservoir productivity prediction method according to claim 3, wherein The step of determining the open flow potential of the target well in the target layer according to the reservoir productivity prediction model and the reservoir fracture parameters of the target well in the target layer includes: Determining the open flow potential of the target well in the target layer according to the number of developed fractures imaged by resistivity logging of the target well in the target layer and the first reservoir productivity prediction model.
5. The gas reservoir productivity prediction method according to claim 1, wherein The reservoir fracture parameters include the fracture development thickness of the reservoir. The step of determining the reservoir fracture parameters of the target well in the target layer according to the logging data of the target well in the target area includes: Determining the fracture development thickness of the reservoir of the target well in the target layer according to the deep lateral resistivity logging data and the natural gamma logging data of the target well.
6. The gas reservoir productivity prediction method according to claim 5, wherein, The reservoir productivity prediction model includes a second reservoir productivity prediction model. The step of establishing a reservoir productivity prediction model based on the reservoir fracture parameters and the open flow potential of each gas well with completed single-well testing in the target layer of the target area includes: Establishing the second reservoir productivity prediction model according to the fracture development thickness and the open flow potential of each gas well with completed single-well testing in the target layer.
7. The gas reservoir productivity prediction method according to claim 6, wherein, The step of determining the open flow potential of the target well in the target layer according to the reservoir productivity prediction model and the reservoir fracture parameters of the target well in the target layer includes: Determining the open flow potential of the target well in the target layer according to the fracture development thickness of the reservoir of the target well in the target layer and the second reservoir productivity prediction model.
8. The gas reservoir productivity prediction method according to claim 5, wherein The step of determining the fracture development thickness of the reservoir of the target well in the target layer according to the deep lateral resistivity logging data and the natural gamma logging data of the target well includes: Taking the section corresponding to the target layer in the target well as the pre-test section; Determine the first ratio between the resistivity and the natural gamma corresponding to the starting depth of each pre-test interval respectively; Determine the second ratio between the resistivity and the natural gamma corresponding to the ending depth of each pre-test interval respectively; Take the difference between the second ratio and the first ratio corresponding to each pre-test interval as the fracture development thickness corresponding to each pre-test interval; Take the sum of the reservoir fracture development thicknesses corresponding to all the pre-test intervals as the reservoir fracture development thickness of the target well in the target horizon.
9. A gas reservoir productivity prediction device, characterized in that, The device includes: A model construction module, configured to establish a reservoir productivity prediction model according to the reservoir fracture parameters and the open flow potential of each gas well that has completed single-well testing in the target horizon in the target area; A parameter determination module, configured to determine the reservoir fracture parameters of the target well in the target horizon according to the logging data of the target well in the target area; A productivity prediction module, configured to determine the open flow potential of the target well in the target horizon according to the reservoir productivity prediction model and the reservoir fracture parameters of the target well in the target horizon.
10. A computer-readable storage medium, characterized in that, The computer program stored in the computer-readable storage medium, when executed by one or more processors, implements the steps of the method according to any one of claims 1 to 8.
11. An electronic device, characterized in that, Comprising a memory and one or more processors, a computer program is stored on the memory, and when the computer program is executed by the one or more processors, the steps of the method according to any one of claims 1 to 8 are executed.