Seismic-electric fusion reservoir fluid identification method
By combining seismic and electrical methods to identify reservoir fluids, the advantages of well-seismic integration and electrical methods are combined to identify reservoir fluids, solving the accuracy and range problems of seismic exploration methods in fluid identification and achieving efficient fluid identification.
Patent Information
- Application Number
- CN202411123451.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-08-15
- Publication Date
- 2026-03-03
AI Technical Summary
In existing technologies, seismic exploration methods are difficult to effectively identify fluids and have low stability, while electrical methods are difficult to identify reservoirs, resulting in low accuracy and limited range of reservoir fluid identification.
A reservoir fluid identification method using seismo-electromagnetic fusion is adopted. Three-dimensional seismic horizons and electromagnetic data are obtained through well-seismic co-location. Combined with geological framework and inverted resistivity profile, fluid types are identified using resistivity fluid type cross-plots.
It achieves high accuracy and wide range in fluid identification, and is simple to operate. It is not limited to the drilling location and can identify fluids in various reservoirs in the work area.
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Abstract
Description
Technical Field
[0001] This invention relates to the field of oil and gas exploration and development technology, and specifically to a reservoir fluid identification method based on seismic-electric fusion. Background Technology
[0002] Currently, geophysical technology research and development in the oil and gas field exploration and development phase is mainly based on seismic methods. Conventional seismic exploration methods, based on differences in rock density or sonic velocity, have been very successful in structural exploration. However, seismic methods are not sensitive to gas saturation, and the low porosity and permeability of tight sandstone gas increase the difficulty of seismic identification, resulting in significant limitations. They are difficult to effectively identify fluids and have low stability, failing to support highly reliable gas-bearing predictions. Electrical resistivity methods are sensitive to fluids, but they are difficult to use for reservoir identification. Well logging can only identify reservoir fluids at the drilling location.
[0003] Therefore, it is of great significance to develop a reservoir fluid identification method with high accuracy, wide identification range and simple operation. Summary of the Invention
[0004] The purpose of this invention is to address the problems of high difficulty and limited identification range in existing reservoir fluid identification methods by providing a seismic-electrical fusion method. Compared to traditional fluid identification techniques, this method combines the advantages of combined well-seismic spatial distribution characterization with the fluid sensitivity of electrical methods. It is not limited to identifying fluids at the drilling location but can accurately identify fluids in various reservoirs within the work area, resulting in high accuracy, a wider identification range, and simple operation.
[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows:
[0006] A reservoir fluid identification method based on seismoelectric fusion includes the following steps:
[0007] The geological framework of the work area is obtained by acquiring three-dimensional seismic horizons through well seismic data and extracting the top and bottom interfaces of the three-dimensional seismic horizons.
[0008] Electromagnetic data of the work area is acquired, and the electromagnetic data is inverted to obtain the inverted resistivity profile of the work area.
[0009] The inversion resistivity of the target layer in the work area is obtained based on the geological framework and inversion resistivity profile of the work area.
[0010] Based on the RT-AC cross-plot, a cross-plot of resistivity fluid types is established;
[0011] The fluid type of the target layer is identified by using the resistivity fluid type cross-plot and the inversion resistivity of the target layer.
[0012] This invention provides a reservoir fluid identification method combining seismic and electrical data fusion. First, three-dimensional seismic horizons are obtained. Based on these horizons, the geological structure of the work area is further derived. Electromagnetic data is inverted to obtain an inversion resistivity profile. Combining the geological structure and the inversion resistivity profile, the inversion resistivity of the target layer in the work area is obtained. Based on a resistivity-fluid type cross-plot, the fluid type of the target layer can be identified from its inversion resistivity. Compared to traditional fluid identification techniques, this method combines the advantages of combined well-seismic spatial distribution characterization with the fluid sensitivity of electrical methods. It is not limited to identifying fluids at drilling locations but can accurately identify fluids in various reservoirs within the work area, resulting in high accuracy, a wide identification range, and simple operation.
[0013] Furthermore, the three-dimensional seismic horizons were obtained using the following method:
[0014] Acquire VSP data, drilling core data, well logging data, well logging data, and synthetic seismic records for the work area;
[0015] Based on the VSP data, the drilling core data, the well logging data, the well logging data, and the synthetic seismic record, a well-seismic joint inversion is performed to calibrate the stratigraphic horizons of the work area and obtain the three-dimensional seismic horizons.
[0016] This invention uses VSP data, drilling core data, well logging data, well logging data, and synthetic seismic records to perform well-seismic joint inversion, which can accurately calibrate the stratigraphic positions of multiple strata in the work area to obtain three-dimensional seismic horizons with higher accuracy.
[0017] Furthermore, the inversion method is the least squares inversion method.
[0018] Furthermore, the synthetic seismic record is obtained through the following steps:
[0019] Extract acoustic logging curves and density logging curves from well logging data;
[0020] The reflection coefficient sequence is calculated based on the acoustic logging curve and the density logging curve.
[0021] Obtain the seismic wavelet of the work area;
[0022] The synthetic seismic record is obtained by convolving the reflection coefficient sequence with the seismic wavelet.
[0023] Furthermore, the seismic wavelets of the work area were obtained using the following method:
[0024] Obtain the seismic traces near the well in the work area;
[0025] Based on the convolution model, the seismic wavelet is obtained according to the reflection coefficient sequence and the well-side seismic trace.
[0026] Furthermore, the convolution model is a model for producing synthetic (theoretical) seismic records. It assumes that each seismic record is composed of the convolution of the seismic wavelet and the reflection functions of each layer of the subsurface model, and random noise can be added if necessary.
[0027] Furthermore, the inverted resistivity profile of the work area was obtained using the following method:
[0028] The electromagnetic data is inverted to obtain an initial inverted resistivity profile;
[0029] Obtain the well logging resistivity curve of the work area;
[0030] Based on the well logging resistivity curve, electrical standard layers are divided on the initial inverted resistivity profile to determine the position of the geological framework on the initial inverted resistivity profile.
[0031] Obtain the frequency parameters corresponding to the geological framework;
[0032] Calculate the overall resistance of the geological framework based on the frequency parameters;
[0033] A transition geoelectric model is established based on the geological framework, the initial inverted resistivity profile, and the comprehensive resistivity.
[0034] The inverted resistivity profile is obtained by inverting the transition geoelectric model.
[0035] Furthermore, the specific steps for inverting the electromagnetic data to obtain the initial inverted resistivity profile are as follows:
[0036] An initial geoelectric model was established based on electromagnetic data;
[0037] Obtain the measured apparent resistivity of the work area;
[0038] Based on the conjugate gradient method, the initial geoelectric model is iteratively calculated using an inversion algorithm until the fitting error is within a preset error range, and the inversion result is output to obtain the initial inverted resistivity profile.
[0039] The fitting error is the difference between the apparent resistivity of the initial geoelectric model and the measured apparent resistivity.
[0040] Furthermore, the measured apparent resistivity is obtained using the following formula:
[0041]
[0042] Where Ex is the x-component of the electric field of the wide-area electromagnetic device, MN is the distance between adjacent receiving points of the wide-area electromagnetic device, I is the magnitude of the harmonic current transmitted by the wide-area electromagnetic device, K is the device coefficient of the observation device of the wide-area electromagnetic device, and F(ikr) is the electromagnetic effect coefficient.
[0043] Furthermore, the device coefficient is obtained by the following formula:
[0044] K = 2πr 3 / (dL·MN),
[0045] Where dL is the distance of the electric dipole source of the wide-area electromagnetic device, and r is the transmit / receive distance of the wide-area electromagnetic device.
[0046] Furthermore, the electromagnetic effect coefficient is obtained by the following formula:
[0047]
[0048] in, θ is the azimuth angle, r is the transmit / receive distance of the wide-area electromagnetic device; k is the wave number, and i is the imaginary unit.
[0049] Furthermore, a resistivity fluid type cross-plot is established, specifically based on the RT-AC cross-plot, multiple resistivity fluid type cross-plots corresponding one-to-one with various reservoir types are established.
[0050] Furthermore, the fluid type of the target layer was specifically identified using the following methods:
[0051] Based on the cross-plot of multiple resistivity fluid types, multiple logging resistivity characteristics corresponding to multiple fluid types are obtained for each reservoir type;
[0052] Obtain the well logging inversion resistivity mapping relationship, which is the relationship between the well logging resistivity and the inversion resistivity of the formation in the work area;
[0053] Based on the well logging inversion resistivity mapping relationship and multiple well logging resistivity features, multiple inversion resistivity features corresponding one-to-one with multiple fluid types are obtained for each reservoir type;
[0054] Using the inverted resistivity characteristics, the fluid type of the target layer is determined based on the inverted resistivity of the target layer.
[0055] The present invention provides a processing device and a computer-readable storage medium that include the above-described identification method.
[0056] A processing device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps corresponding to the above-described seismoelectric fusion reservoir fluid identification method.
[0057] A computer-readable storage medium storing computer program instructions, which, when executed by a processor, are used to implement the steps corresponding to the above-described reservoir fluid identification method based on seismoelectric fusion.
[0058] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:
[0059] This invention provides a reservoir fluid identification method combining seismic and electrical data fusion. First, three-dimensional seismic horizons are obtained. Based on these horizons, the geological structure of the work area is further derived. Electromagnetic data is inverted to obtain an inversion resistivity profile. Combining the geological structure and the inversion resistivity profile, the inversion resistivity of the target layer in the work area is obtained. Based on a resistivity-fluid type cross-plot, the fluid type of the target layer can be identified from its inversion resistivity. Compared to traditional fluid identification techniques, this method combines the advantages of combined well-seismic spatial distribution characterization with the fluid sensitivity of electrical methods. It is not limited to identifying fluids at drilling locations but can accurately identify fluids in various reservoirs within the work area, resulting in high accuracy, a wide identification range, and simple operation. Attached Figure Description
[0060] Figure 1 This is a flowchart of the reservoir fluid identification method in Example 1.
[0061] Figure 2 This is a geological framework diagram showing the known spatial distribution of the work area in Example 1.
[0062] Figure 3 This is a flowchart of the synthetic seismic record obtained in Example 1.
[0063] Figure 4 This is a flowchart of obtaining the seismic wavelet of the work area in Example 1.
[0064] Figure 5 This is a flowchart of the inversion resistivity profile of the work area obtained in Example 1.
[0065] Figure 6 This is a flowchart of obtaining the initial inverted resistivity profile in Example 1.
[0066] Figure 7 This is a cross-plot of resistivity fluid types corresponding to a type of reservoir in Example 1.
[0067] Figure 8This is a diagram showing the resistivity mapping relationship obtained from well logging in Example 1.
[0068] Figure 9 This is the inversion resistivity diagram of the target layer in Example 1.
[0069] Figure 10 This is a diagram showing the fluid identification results of the target layer in Example 1. Detailed Implementation
[0070] The present invention will now be described in detail with reference to the accompanying drawings.
[0071] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.
[0072] In the description of this application, it should be understood that the orientation descriptions, such as up, down, etc., are based on the orientation or positional relationship shown in the accompanying drawings, and are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of this application.
[0073] In the description of this application, "multiple" refers to two or more. The use of "first" and "second" is for the purpose of distinguishing technical features only and should not be construed as indicating or implying relative importance, or implicitly indicating the number of technical features indicated, or the order in which the technical features are indicated.
[0074] In the description of this application, unless otherwise expressly defined, terms such as "setup," "installation," and "connection" should be interpreted broadly, and those skilled in the art can reasonably determine the specific meaning of the above terms in this application in conjunction with the specific content of the technical solution.
[0075] Example 1
[0076] The following reference Figure 1 A reservoir fluid identification method according to an embodiment of this application is described.
[0077] The reservoir fluid identification method in this application embodiment, such as Figure 1 As shown, steps S100, S200, S300, S400, S500, S600, S700, and S800 are included.
[0078] Step S100: Acquire VSP data, drilling core data, well logging data, well logging data, and synthetic seismic records for the work area;
[0079] In this step, VSP data is acquired using the Vertical Seismic Profiling (VSP) method, a method in which a geophone is placed along the borehole to receive seismic waves inside the formation. Compared to seismic waves received at the surface, VSP data has advantages such as higher signal-to-noise ratio, higher resolution, and clearer kinematic and dynamic characteristics of the waves. Because the seismic records received in the VSP observation system only pass through a low-velocity zone, the energy loss of seismic waves, especially high-frequency components, is reduced compared to surface seismic data, resulting in higher resolution. VSP technology provides the most direct correspondence between subsurface stratigraphic structure and surface measurement parameters, providing accurate time-depth conversion and velocity models for surface seismic data processing and interpretation. It can reliably identify the geological horizons of seismic reflection layers, improve the interpretation of surface seismic data, and even allow for direct study of lithology and reservoir properties using VSP data.
[0080] Core data is obtained through well drilling technology. Core drilling refers to the process of extracting large rock samples (cores) from the ground during drilling to directly obtain reliable information about underground rock formations and understand subsurface geological conditions. The obtained cores directly reflect the true characteristics of the geological strata and are the most reliable and fundamental research data.
[0081] Well logging data is obtained through well logging technology. Well logging, also known as geophysical logging, is a method of measuring geophysical parameters by utilizing the electrochemical, electrical, acoustic, and radioactive properties of rock formations. It belongs to the category of applied geophysical methods. During oil drilling, well logging, also known as well completion logging, must be performed after drilling to the designed well depth to obtain various petroleum geological and engineering technical data, serving as the raw data for well completion and oilfield development.
[0082] Well logging data is obtained through geological logging technology. Geological logging, or simply logging, refers to the direct and indirect collection and recording of downhole information throughout the drilling process, followed by comprehensive analysis to clarify the location, thickness, and fluid properties of oil and gas reservoirs. This provides sufficient evidence for cementing, well testing, and determining the depth of completion. Geological logging is an important tool in conjunction with drilling for oil and gas exploration. It is a method that utilizes various data and parameters to observe, detect, judge, and analyze the properties of underground rocks and the presence of oil and gas during the drilling process.
[0083] Synthetic seismic records are a widely used technique in seismic modeling and form the basis for tasks such as stratigraphic calibration and reservoir characterization. They serve as an intermediary for converting geological models into seismic information. Synthetic seismic records act as a bridge between high-resolution well logging data and regional seismic information, and their accuracy directly impacts the precise calibration of geological stratigraphic levels.
[0084] Step S200: Based on VSP data, drilling core data, logging data, well logging data and synthetic seismic records, perform well-seismic joint inversion to calibrate the stratigraphic horizons of the work area and obtain the three-dimensional seismic horizons;
[0085] In this embodiment, during the well-seismic joint inversion process, the well logging data first needs to undergo environmental correction and standardization processing regarding drilling fluid properties, mud invasion, well temperature, and well diameter to ensure that the logging data represents the true logging response of the subsurface rock strata. Secondly, it is necessary to ensure that the quality of the synthetic seismic record and extracted seismic wavelet meets the requirements. By comprehensively utilizing VSP data, synthetic seismic records, drilling core data, well logging data, and well logging data, the stratigraphic horizons in the work area are accurately calibrated to obtain a depth-accurate three-dimensional seismic horizon.
[0086] Understandably, the stratigraphic horizon of a work area refers to the comprehensive analysis and research conducted by geologists within the research area, based on measured profile data and considering factors such as lithology, fossils, and contact relationships. This involves a detailed study of the lithology of each sub-layer from bottom to top along the measured profile, identifying patterns of lithological variation, merging several sub-layers into larger layers, and then analyzing the fossil assemblages within these layers. Particular attention is paid to the stratified fossils, which, combined with rock assemblages exhibiting patterns of lithological variation, allow for the identification of stratigraphic units and the determination of their relative geological ages.
[0087] Step S300: Extract the top and bottom interfaces of the three-dimensional seismic horizons to obtain the geological framework;
[0088] In this step, such as Figure 2 As shown, the top and bottom interfaces are extracted from the three-dimensional seismic horizons to obtain the geological framework of the known spatial distribution of the work area.
[0089] Step S400: Obtain electromagnetic data for the work area;
[0090] In this step, electromagnetic data is obtained using the wide-area electromagnetic method.
[0091] Step S500: Invert the electromagnetic data to obtain the inverted resistivity profile of the work area;
[0092] In this step, the inverted resistivity profile of the work area can be obtained by inverting the electromagnetic data. The inverted resistivity profile map can accurately describe the resistivity characteristics of different depths of the work area profile.
[0093] Step S600: Obtain the inversion resistivity of the target layer in the work area based on the geological framework and the inversion resistivity profile;
[0094] In this embodiment, the inversion resistivity of the target layer in the work area is obtained by utilizing the geological framework and inversion resistivity profile of the work area.
[0095] Step S700: Based on the RT-AC cross-plot, establish a cross-plot of resistivity fluid types;
[0096] In this step, the resistivity-fluid type cross-plot can represent the correspondence between formation resistivity and fluid type. By establishing the resistivity-fluid type cross-plot, the target layer in the work area can be identified using the resistivity-fluid type cross-plot in subsequent steps.
[0097] Step S800: Identify the fluid type of the target layer using the resistivity fluid type cross-plot and the inversion resistivity of the target layer.
[0098] In this step, the fluid type of the target layer is identified based on the resistivity fluid type cross-plot and the inversion resistivity of the target layer.
[0099] In this embodiment, a combined well-seismic inversion is performed using VSP data, drilling core data, well logging data, well logging data, and synthetic seismic records. This allows for accurate calibration of the stratigraphic levels of multiple formations in the work area, resulting in three-dimensional seismic horizons. Based on these three-dimensional seismic horizons, the geological framework of the work area is further derived. Electromagnetic data is inverted to obtain an inversion resistivity profile. Combining the geological framework and the inversion resistivity profile, the inversion resistivity of the target layer in the work area is obtained. Based on the resistivity-fluid type cross-plot, the fluid type of the target layer can be identified from its inversion resistivity. Compared to traditional fluid identification techniques, the reservoir fluid identification method of this application integrates the advantages of combined well-seismic spatial distribution characterization and the sensitivity of electrical resistivity to fluids. It is not limited to identifying fluids at drilling locations but can accurately identify fluids in various reservoirs within the work area.
[0100] One embodiment of this application, such as Figure 3 As shown, the synthetic seismic record in step S100 is obtained through the following steps.
[0101] Step S110: Extract sonic logging curves and density logging curves from the logging data;
[0102] Step S120: Calculate the reflection coefficient sequence based on the acoustic logging curve and the density logging curve;
[0103] Step S130: Obtain the seismic wavelet of the work area;
[0104] Step S140: Convolve the reflection coefficient sequence with the seismic wavelet to obtain a synthetic seismic record.
[0105] In this embodiment, the seismic wavelet is a signal with a definite start time, finite energy, and a certain duration; it is the basic unit in the seismic record. Acoustic logging curves and density logging curves are extracted from well logging data, and the reflection coefficient sequence is calculated. Convolution of the reflection coefficient sequence and the seismic wavelet yields the synthetic seismic record.
[0106] In this embodiment, the seismic reflection coefficient R refers to the ratio of the reflected wave amplitude to the incident wave amplitude, and the calculation formula is as follows:
[0107]
[0108] Z = ρ·V
[0109] In the formula: AR and Ai represent the amplitude of the reflected wave and the amplitude of the incident wave, respectively; Zn represents the wave impedance of the nth stratum; Zn-1 represents the wave impedance of the (n-1)th stratum; ρ represents the density of the stratum; and V represents the layer velocity.
[0110] It is understandable that obtaining a synthetic seismic record is a simplified one-dimensional forward modeling process. The synthetic seismic record is the result of the convolution of the seismic wavelet and the reflection coefficient. The specific calculation formula is as follows:
[0111] S(t)=R(t)×W(t),
[0112] Where S(t) is the synthetic seismic record, R(t) is the reflection coefficient sequence, and W(t) is the seismic wavelet.
[0113] It should be noted that after obtaining the synthetic seismic record, it needs to be corrected based on a more accurate velocity field, and then matched and adjusted with the well-side seismic trace to ensure the accuracy of the synthetic seismic record.
[0114] One embodiment of this application, such as Figure 4 As shown, the step S130 of "obtaining the seismic wavelet of the work area" will be further explained. Step S130 includes, but is not limited to, steps S131 and S132.
[0115] Step S131: Obtain the seismic traces near the well in the work area;
[0116] Step S132: Based on the convolution model, obtain the seismic wavelet according to the reflection coefficient sequence and the seismic traces near the well.
[0117] In this embodiment, the seismic wavelet is obtained by acquiring the seismic trace near the well and combining the reflection coefficient data with the seismic trace near the well using the convolution model.
[0118] One embodiment of this application further explains step S500, "inverting electromagnetic data to obtain the inverted resistivity profile of the work area," as follows: Figure 5 As shown, step S500 includes, but is not limited to, steps S510, S520, S530, S540, S550, S560 and S570.
[0119] Step S510: Invert the electromagnetic data to obtain the initial inverted resistivity profile;
[0120] Step S520: Obtain the well logging resistivity curve of the work area;
[0121] Step S530: Based on the well logging resistivity curve, divide the electrical standard layer on the initial inversion resistivity profile to determine the position of the geological framework on the initial inversion resistivity profile;
[0122] Step S540: Obtain the frequency parameters corresponding to the geological framework;
[0123] Step S550: Calculate the overall resistance of the geological framework based on the frequency parameters;
[0124] Step S560: Establish a transition geoelectric model based on the geological framework, initial inverted resistivity profile, and comprehensive resistivity;
[0125] Step S570: Invert the transition geoelectric model to obtain the inverted resistivity profile.
[0126] In this embodiment, an initial inverted resistivity profile is obtained by inverting electromagnetic data. However, due to depth deviations in the initial inverted resistivity profile, it cannot accurately describe the resistivity characteristics of the target layer. Therefore, by combining the characteristics of well logging resistivity curves, electrical standard layer interfaces are precisely delineated on the initial inverted resistivity profile to determine the location of the geological framework of the work area on the initial inverted resistivity profile. Then, based on the geological framework, the initial inverted resistivity profile, and the comprehensive resistivity, a transitional geoelectric model is established to invert the initial inverted resistivity profile, resulting in an inverted resistivity profile that accurately describes the resistivity characteristics at different depths within the work area.
[0127] One embodiment of this application further explains the step S510, "inverting electromagnetic data to obtain an initial inverted resistivity profile," as follows: Figure 6 As shown, step S510 includes, but is not limited to, steps S511, S512 and S513.
[0128] Step S511: Establish an initial geoelectric model based on electromagnetic data;
[0129] Step S512: Obtain the measured apparent resistivity of the work area;
[0130] Step S513: Based on the conjugate gradient method, the initial geoelectric model is iteratively calculated using the inversion algorithm until the fitting error is within the preset error range. The inversion result is then output to obtain the initial inverted resistivity profile. The fitting error is the difference between the apparent resistivity of the initial geoelectric model and the measured apparent resistivity.
[0131] In this embodiment, the linear conjugate gradient method is a highly effective iterative algorithm for solving equations. This method constructs a series of conjugate directions using gradient vectors, thereby allowing the objective function to reach its extreme point. The initial geoelectric model is inverted using an inversion algorithm, and iterative calculations are performed based on the conjugate gradient method until the fitting error is within a preset error range. This confirms that the initial geoelectric model can reflect the actual situation of the work area, and the inversion results are output to obtain the initial inverted resistivity profile.
[0132] In one embodiment of this application, the apparent resistivity measured in step S512 is obtained using the following formula:
[0133]
[0134] Where Ex is the x-component of the electric field of the wide-area electromagnetic device, MN is the distance between adjacent receiving points of the wide-area electromagnetic device, I is the magnitude of the harmonic current transmitted by the wide-area electromagnetic device, K is the device coefficient of the observation device of the wide-area electromagnetic device, and F(ikr) is the electromagnetic effect coefficient.
[0135] In one embodiment of this application, the device coefficient is obtained by the following formula:
[0136] K = 2πr 3 / (dL·MN),
[0137] Where dL is the distance of the electric dipole source of the wide-area electromagnetic device, and r is the transmit / receive distance of the wide-area electromagnetic device.
[0138] In one embodiment of this application, the electromagnetic effect coefficient is obtained by the following formula:
[0139]
[0140] in, θ is the azimuth angle, r is the transmit / receive distance of the wide-area electromagnetic device; k is the wave number, and i is the imaginary unit.
[0141] In one embodiment of this application, the step S700 of “establishing a resistivity fluid type cross-plot based on the RT-AC cross-plot” is further explained. Step S700 includes, but is not limited to, step S710.
[0142] Step S710: Based on the RT-AC cross-plot, establish multiple resistivity fluid type cross-plots that correspond one-to-one with various reservoir types.
[0143] In this step, reservoir types include Class I, Class II, and Class III reservoirs. Since the correspondence between formation resistivity and fluid type differs for different reservoir types, resistivity-fluid type cross-plots are established for Class I, Class II, and Class III reservoirs based on RT-AC cross-plots, enabling fluid identification for different reservoir types. For example... Figure 7As shown, Figure 7 This is a cross-plot of resistivity fluid types corresponding to a certain type of reservoir.
[0144] In one embodiment of this application, the step S800, "identifying the fluid type of the target layer by using the resistivity fluid type cross-plot and the inversion resistivity of the target layer", is further explained. Step S800 includes, but is not limited to, steps S810, S820, S830 and S840.
[0145] Step S810: Based on the cross-plot of multiple resistivity fluid types, obtain multiple logging resistivity characteristics for each reservoir type that correspond one-to-one with multiple fluid types;
[0146] In this step, well logging resistivity features are extracted from the resistivity-fluid type cross plot corresponding to each reservoir type. For example, the well logging resistivity features in the resistivity-fluid type cross plot corresponding to a certain type of reservoir are: the well logging resistivity of gas layer is greater than 60 Ω·m, the well logging resistivity of gas-water co-layer is 30 Ω·m to 60 Ω·m, and the well logging resistivity of water layer is less than 30 Ω·m.
[0147] Step S820: Obtain the well logging inversion resistivity mapping relationship, which is the relationship between the well logging resistivity and the inversion resistivity of the formation in the work area;
[0148] In this step, such as Figure 8 As shown, Figure 8 This is a mapping diagram of well logging inversion resistivity, based on the correspondence between well logging resistivity and inversion resistivity at the same location in the formation, i.e., the mapping relationship of well logging inversion resistivity.
[0149] Step S830: Based on the logging inversion resistivity mapping relationship and multiple logging resistivity features, obtain multiple inversion resistivity features that correspond one-to-one with multiple fluid types in each reservoir type;
[0150] In this step, the inversion resistivity characteristics can be obtained based on the mapping relationship between well logging inversion resistivity and the well logging resistivity characteristics. For example, based on the well logging resistivity characteristics corresponding to a certain type of reservoir, the inversion resistivity characteristics of that type of reservoir can be obtained through the mapping relationship between well logging inversion resistivity: the inversion resistivity of gas layers in this type of reservoir is greater than 85 Ω·m, the inversion resistivity of gas-water co-layers is 85 Ω·m to 35 Ω·m, and the inversion resistivity of water layers is less than 35 Ω·m.
[0151] Step S840: Utilize the inversion resistivity characteristics to determine the fluid type of the target layer based on the inversion resistivity of the target layer.
[0152] In this step, such as Figure 9 As shown, Figure 9The inversion resistivity map of the target layer is used. By comparing the inversion resistivity of the target layer with the inversion resistivity characteristics of the corresponding reservoir type, the fluid type of the target layer can be determined. Figure 10 As shown, Figure 10 The image shows the fluid identification results for the target layer.
[0153] In addition, one embodiment of this application discloses a computer-readable storage medium storing a processor-executable program, which, when executed by a processor, is used to implement the reservoir fluid identification method described above.
[0154] In this embodiment, a combined well-seismic inversion is performed using VSP data, drilling core data, logging data, well logging data, and synthetic seismic records. This allows for accurate calibration of the stratigraphic levels of multiple formations in the work area, resulting in three-dimensional seismic horizons. Based on these three-dimensional seismic horizons, the geological framework of the work area is further derived. Electromagnetic data is inverted to obtain an inversion resistivity profile. Combining the geological framework and the inversion resistivity profile, the inversion resistivity of the target layer in the work area is obtained. Based on the resistivity-fluid type cross-plot, the fluid type of the target layer can be identified from its inversion resistivity. The computer-readable storage medium of this embodiment combines the advantages of combined well-seismic spatial distribution characterization with the fluid sensitivity of electrical resistivity methods. It is not limited to identifying fluids at drilling locations but can accurately identify fluids in various reservoirs within the work area.
[0155] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.
Claims
1. A reservoir fluid identification method based on seismoelectric fusion, characterized in that, Includes the following steps: The geological framework of the work area is obtained by acquiring three-dimensional seismic horizons through well seismic data and extracting the top and bottom interfaces of the three-dimensional seismic horizons. Electromagnetic data of the work area is acquired, and the electromagnetic data is inverted to obtain the inverted resistivity profile of the work area. The inversion resistivity of the target layer in the work area is obtained based on the geological framework and inversion resistivity profile of the work area. Based on the RT-AC cross-plot, a cross-plot of resistivity fluid types is established; The fluid type of the target layer is identified by using the resistivity fluid type cross-plot and the inversion resistivity of the target layer.
2. The reservoir fluid identification method based on seismoelectric fusion according to claim 1, characterized in that, The three-dimensional seismic horizon was obtained using the following method: Acquire VSP data, drilling core data, well logging data, well logging data, and synthetic seismic records for the work area; Based on the VSP data, the drilling core data, the well logging data, the well logging data, and the synthetic seismic record, a well-seismic joint inversion is performed to calibrate the stratigraphic horizons of the work area and obtain the three-dimensional seismic horizons.
3. The reservoir fluid identification method based on seismoelectric fusion according to claim 2, characterized in that, Synthetic seismic records are obtained through the following steps: Extract acoustic logging curves and density logging curves from well logging data; The reflection coefficient sequence is calculated based on the acoustic logging curve and the density logging curve. Obtain the seismic wavelet of the work area; The synthetic seismic record is obtained by convolving the reflection coefficient sequence with the seismic wavelet.
4. The reservoir fluid identification method based on seismoelectric fusion according to claim 3, characterized in that, The seismic wavelet of the work area was obtained using the following method: Obtain the seismic traces near the well in the work area; Based on the convolution model, the seismic wavelet is obtained according to the reflection coefficient sequence and the well-side seismic trace.
5. The reservoir fluid identification method based on seismoelectric fusion according to claim 1, characterized in that, The inverse resistivity profile of the work area was obtained using the following method: The electromagnetic data is inverted to obtain an initial inverted resistivity profile; Obtain the well logging resistivity curve of the work area; Based on the well logging resistivity curve, electrical standard layers are divided on the initial inverted resistivity profile to determine the position of the geological framework on the initial inverted resistivity profile. Obtain the frequency parameters corresponding to the geological framework; Calculate the overall resistance of the geological framework based on the frequency parameters; A transition geoelectric model is established based on the geological framework, the initial inverted resistivity profile, and the comprehensive resistivity. The inverted resistivity profile is obtained by inverting the transition geoelectric model.
6. The reservoir fluid identification method based on seismoelectric fusion according to claim 5, characterized in that, The specific steps for inverting the electromagnetic data to obtain the initial inverted resistivity profile are as follows: An initial geoelectric model was established based on electromagnetic data; Obtain the measured apparent resistivity of the work area; Based on the conjugate gradient method, the initial geoelectric model is iteratively calculated using an inversion algorithm until the fitting error is within a preset error range, and the inversion result is output to obtain the initial inverted resistivity profile. The fitting error is the difference between the apparent resistivity of the initial geoelectric model and the measured apparent resistivity.
7. The reservoir fluid identification method based on seismoelectric fusion according to any one of claims 1-6, characterized in that, Establish resistivity fluid type cross-plots, specifically based on the RT-AC cross-plot, to establish multiple resistivity fluid type cross-plots that correspond one-to-one with various reservoir types.
8. The reservoir fluid identification method based on seismoelectric fusion according to claim 7, characterized in that, The fluid type of the target layer was specifically identified using the following method. Based on the cross-plot of multiple resistivity fluid types, multiple logging resistivity characteristics corresponding to multiple fluid types are obtained for each reservoir type; Obtain the well logging inversion resistivity mapping relationship, which is the relationship between the well logging resistivity and the inversion resistivity of the formation in the work area; Based on the well logging inversion resistivity mapping relationship and multiple well logging resistivity features, multiple inversion resistivity features corresponding one-to-one with multiple fluid types are obtained for each reservoir type; Using the inverted resistivity characteristics, the fluid type of the target layer is determined based on the inverted resistivity of the target layer.
9. A processing apparatus comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps corresponding to the reservoir fluid identification method of seismoelectric fusion as described in any one of claims 1-8.
10. A computer-readable storage medium storing computer program instructions thereon, characterized in that, When the computer program instructions are executed by the processor, they are used to implement the steps corresponding to the reservoir fluid identification method of seismoelectric fusion as described in any one of claims 1-8.