Oil-gas exploration method based on fracture-cavity body identification

Abstract

The invention relates to the technical field of oil-gas exploration methods, in particular to an oil-gas exploration method based on fracture-cavity body recognition. The oil-gas exploration method based on fracture-cavity body recognition aims at solving the technical problems that in the prior art, an area with the fast sedimentary facies change cannot be accurately predicted, the influence on inversion precision is large, and the quantitative interpretation error is large. According to the technical scheme, the oil-gas exploration method based on fracture-cavity body recognition comprises the following steps of S1, well-seismic calibration; s2, seismic facies analysis; s3, performing phase control modeling; s4, phase control random optimization inversion is carried out; s5, fracture-cavity carving is carried out; and S6, reserve estimation. According to the method, a random optimization algorithm is adopted, so that a synthetic seismic record and an actual seismic record are similar to each other to the greatest extent, the purpose of inversion prediction is achieved, the iteration and convergence speed is increased, high-frequency components and low-frequency components of known well data can be well used for making up for the defect of seismic frequency band width, and the seismic performance is improved. And the result is more consistent with the actual underground geological condition.

Description

Technical Field

[0001] The present invention relates to the technical field of oil and gas exploration methods, and in particular to an oil and gas exploration method based on fracture-cavity body identification. Background Art

[0002] Oil and gas exploration refers to the process of finding, evaluating and developing underground oil and gas resources through geological exploration and engineering technology. Since the distribution of oil and gas resources is random and uncertain, the selection and application of oil and gas exploration methods are particularly important. Oil and gas exploration methods include geological exploration methods, geophysical exploration methods and drilling exploration methods. These methods provide basic data and technical support for the development of oil and gas fields by detecting and evaluating underground oil and gas resources. However, the existing exploration methods only use seismic interpretation layers as constraints, and interpolate and extrapolate the logging information according to the inverse distance weighted method. This is a gradual prediction, and it is impossible to accurately predict areas with large thickness changes between wells and rapid sedimentary phase changes. It has a great impact on the inversion accuracy and large quantitative interpretation errors. Summary of the invention

[0003] In view of the shortcomings of the prior art, the purpose of the present invention is to provide an oil and gas exploration method based on fracture-cavity identification, which aims to solve the technical problems that areas with rapid sedimentary phase changes cannot be accurately predicted under the prior art, which has a great impact on inversion accuracy and has large quantitative interpretation errors.

[0004] The technical solution of the present invention is: an oil and gas exploration method based on fracture-cavity body identification, the steps of which are as follows:

[0005] S1: well seismic calibration;

[0006] S11: Seismic data reprocessing, including denoising, surface consistent amplitude compensation and random denoising;

[0007] S12: Correcting the acoustic logging curve;

[0008] S13: Real depth conversion and reflection coefficient calculation

[0009] S131: The formula for real depth conversion using the integral of the acoustic time difference curve is: Where H0 is the starting depth in meters, H is the ending depth in meters, T0 is the time corresponding to the starting depth in seconds, T h is the time corresponding to the depth H, in seconds, Δt(h) is the acoustic time difference corresponding to the depth, in ms;

[0010] S132: The difference in wave impedance between the upper and lower layers determines the formation transmission coefficient, which is expressed as: where ρi 、V i is the density and velocity of the i-th layer on the logging curve, ρ i+1 and V i+1 is the density and velocity of the i+1th layer, ρ and V are derived from the velocity and density logging curves;

[0011] S2: Seismic phase analysis

[0012] S21: Search for the foreset reflection structure;

[0013] S22: Classification of non-progradational structures, including parallel structures, subparallel-random hillock structures, divergent structures, chaotic structures and non-reflective structures;

[0014] S23: Determine the spatial morphology of the reflection structure, including: mat-shaped, wedge-shaped, hillock-shaped, filling and lens-shaped;

[0015] S24: Rationality analysis of the combination of reflection structure and appearance, the combination of the same phase unit, the principle of structure and appearance combination analysis along the strike and dip is: the energy level must match, that is, the reflection structure and appearance of the unified sedimentary body must be at the same energy level;

[0016] S25: Amplitude, frequency and continuity are combined to identify their characteristic changes and divide seismic phases;

[0017] S3: Phased Modeling

[0018] S31: extracting the phase boundary of each sedimentary microfacies from the sedimentary phase map, and assigning the phase boundary line a coordinate value consistent with the work area according to the coordinate ratio relationship with the actual work area;

[0019] S32: Project the phase boundary to the corresponding small layer in the isochronous stratigraphic framework, assign a specific integer value to the grid delineated by the phase boundary in the small layer as its phase code, which is used to identify the microfacies, and different sedimentary microfacies are assigned different phase codes;

[0020] S33: digitizing the sedimentary facies maps of different layers into the isochronous stratigraphic framework model according to the above steps, and finally forming a three-dimensional sedimentary facies model;

[0021] S4: Phase-controlled random optimization inversion, based on seismic data, combined with sedimentary facies knowledge, with well logging data of known wells as constraints, uses random optimization algorithms to maximize the similarity between synthetic seismic records and actual seismic records for inversion prediction;

[0022] S5: Fracture carving, the carving methods include: the method combining the seismic attribute volume with the seismic inversion volume and the seismic fracture carving method based on geological modeling;

[0023] S51: Combine the seismic attribute volume and the seismic inversion volume to perform fracture and hole carving. The steps are as follows:

[0024] S511: Based on the fidelity seismic data volume and combined with reservoir well seismic calibration, the seismic reflection characteristics of effective reservoirs are identified and classified;

[0025] S512: Based on the optimization of seismic sensitive attributes and the carving threshold value test, the three-dimensional geometric morphology of seismic facies of different reservoir types is carved out;

[0026] S513: Carry out joint well-seismic inversion to obtain the seismic effective porosity volume;

[0027] S514: Calculate and classify the effective porosity volume of the reservoir;

[0028] S515: Prepare maps based on the results of reserve calculation unit division;

[0029] S52: Seismic fracture and cave carving method using geological modeling, the steps are as follows:

[0030] S521: Based on the guaranteed seismic data volume and in combination with reservoir well seismic calibration, the seismic reflection characteristics of the effective reservoir are identified and classified;

[0031] S522: Based on the optimization of seismic sensitive attributes and the carving threshold value test, the three-dimensional geometric morphology of seismic facies of different reservoir types is carved out;

[0032] S523: Under the guidance of geological modeling ideas, the three-dimensional geometric morphology of seismic phases of different reservoir types is transformed into a three-dimensional geometric structure model of fracture-cavity bodies;

[0033] S524: Conduct well-seismic joint seismic inversion to obtain seismic wave impedance constraint modeling volume;

[0034] S525: Obtaining a geological model of effective porosity of fracture-cavity bodies;

[0035] S526: Based on the result of the reserve calculation unit division, the area, effective thickness and effective porosity of different reservoir spaces in the reserve calculation unit are calculated by using the classified reservoir effective porosity body, and a map is compiled;

[0036] S6: Reserves estimation.

[0037] As a preferred method, in S12, the base value analysis method is used to correct the logging curves with stable formation thickness and clear mudstone standard layers; the probability statistics method and regression relationship method are used for those with unclear seismic characteristics and fast reservoir changes; and the dewiring smoothing filter method is used for abnormal values ​​of individual wells.

[0038] Preferably, the energy levels of the reflection structure in S24 are: parallel structure - low energy, sub-parallel-random hillock structure - low energy to high energy, divergent structure - high energy, chaotic structure - high energy to extremely high energy, non-reflective structure - low energy to extremely high energy, and the energy levels of the phase unit shapes are: mat-shaped - from low energy to high energy, wedge-shaped - from low energy to high energy, hillock-shaped - high energy, filling - low energy or high energy, lens-shaped - high energy.

[0039] As a preferred method, the phase-controlled random optimization inversion in S4 starts from the well-constrained geological model and uses the improved random hill climbing method for global optimization, so that the seismic data synthesized by the modified geological model forward model and the actual observed seismic data can achieve the best match. The improved random hill climbing method can perform sparse point search at high temperature and search in the current model at low temperature each time the model is iterated and updated.

[0040] Preferably, the method for calculating the effective porosity body of the classified reservoir in S514 is: merging the classified seismic phase geometric morphology carving results and the corresponding seismic porosity body and obtaining the intersection.

[0041] Preferably, the map preparation in S515 also requires using the classified reservoir effective porosity body to obtain the area, effective thickness and effective porosity of different reservoir spaces in the reserve calculation unit.

[0042] Preferably, S525 obtains a geological model of effective porosity of the fracture-cavity body based on single well logging modeling and seismic wave impedance constraint modeling combined with a three-dimensional geometric structure model of the fracture-cavity body.

[0043] Preferably, the reserve estimation in S6 adopts the fracture-cavity carving volumetric method.

[0044] The invention has the following beneficial effects: the random optimization algorithm is adopted to make the synthetic seismic record and the actual seismic record achieve the maximum similarity, so as to achieve the purpose of inversion prediction; starting from the well-constrained geological model, the improved random hill climbing method is adopted to globally optimize, so that the seismic data synthesized by the modified geological model forward modeling and the actual observed seismic data can achieve the best match; the improved random hill climbing method can perform sparse point search at high temperature and search in the current model at low temperature each time the model is iterated and updated, thereby accelerating the iteration and convergence speed, and can better use the high-frequency components and low-frequency components of the known well data to compensate for the In order to make up for the insufficiency of seismic bandwidth, we invert high-resolution wave impedance profiles and improve the accuracy of inter-well reservoir prediction. We start with multiple random solutions to form a set of optimal solutions, and then find the global optimal solution from them, so that the synthetic seismic records and the observed seismic records can be as similar as possible, achieving the goal of global optimal inversion prediction, avoiding the disadvantage of the conventional random hill climbing method that easily produces local optimal solutions, adding sedimentary phase control to establish a low-frequency model, comprehensively utilizing geological information and the laws of inter-well reservoir development, and considering the sedimentary microfacies model's ability to characterize the attribute parameter plane. The results are more consistent with the actual underground geological conditions. DETAILED DESCRIPTION

[0045] The present invention will be further described below in conjunction with the embodiments.

[0046] The present invention provides an embodiment: an oil and gas exploration method based on fracture-cavity body identification, the steps of which are as follows:

[0047] S1: well seismic calibration;

[0048] S11: Seismic data reprocessing, including denoising, surface consistent amplitude compensation and random denoising;

[0049] S12: Correcting the acoustic logging curve;

[0050] S13: Real depth conversion and reflection coefficient calculation

[0051] S131: The formula for real depth conversion using the integral of the acoustic time difference curve is: Where H0 is the starting depth in meters, H is the ending depth in meters, T0 is the time corresponding to the starting depth in seconds, T h is the time corresponding to the depth H, in seconds, Δt(h) is the acoustic time difference corresponding to the depth, in ms;

[0052] S132: The difference in wave impedance between the upper and lower layers determines the formation transmission coefficient, which is expressed as: where ρ i 、V iis the density and velocity of the i-th layer on the logging curve, ρ i+1 and V i+1 is the density and velocity of the i+1th layer, ρ and V are derived from the velocity and density logging curves;

[0053] S2: Seismic phase analysis

[0054] S21: Search for the foreset reflection structure;

[0055] S22: Classification of non-progradational structures, including parallel structures, subparallel-random hillock structures, divergent structures, chaotic structures and non-reflective structures;

[0056] S23: Determine the spatial morphology of the reflection structure, including: mat-shaped, wedge-shaped, hillock-shaped, filling and lens-shaped;

[0057] S24: Rationality analysis of the combination of reflection structure and appearance, the combination of the same phase unit, the principle of structure and appearance combination analysis along the strike and dip is: the energy level must match, that is, the reflection structure and appearance of the unified sedimentary body must be at the same energy level;

[0058] S25: Amplitude, frequency and continuity are combined to identify their characteristic changes and divide seismic phases;

[0059] S3: Phased Modeling

[0060] S31: extracting the phase boundary of each sedimentary microfacies from the sedimentary phase map, and assigning the phase boundary line a coordinate value consistent with the work area according to the coordinate ratio relationship with the actual work area;

[0061] S32: Project the phase boundary to the corresponding small layer in the isochronous stratigraphic framework, assign a specific integer value to the grid delineated by the phase boundary in the small layer as its phase code, which is used to identify the microfacies, and different sedimentary microfacies are assigned different phase codes;

[0062] S33: digitizing the sedimentary facies maps of different layers into the isochronous stratigraphic framework model according to the above steps, and finally forming a three-dimensional sedimentary facies model;

[0063] S4: Phase-controlled random optimization inversion, based on seismic data, combined with sedimentary facies knowledge, with well logging data of known wells as constraints, uses random optimization algorithms to maximize the similarity between synthetic seismic records and actual seismic records for inversion prediction;

[0064] S5: Fracture carving, the carving methods include: the method combining the seismic attribute volume with the seismic inversion volume and the seismic fracture carving method based on geological modeling;

[0065] S51: Combine the seismic attribute volume and the seismic inversion volume to perform fracture and hole carving. The steps are as follows:

[0066] S511: Based on the fidelity seismic data volume and combined with reservoir well seismic calibration, the seismic reflection characteristics of effective reservoirs are identified and classified;

[0067] S512: Based on the optimization of seismic sensitive attributes and the carving threshold value test, the three-dimensional geometric morphology of seismic facies of different reservoir types is carved out;

[0068] S513: Carry out joint well-seismic inversion to obtain the seismic effective porosity volume;

[0069] S514: Calculate and classify the effective porosity volume of the reservoir;

[0070] S515: Prepare maps based on the results of reserve calculation unit division;

[0071] S52: Seismic fracture and cave carving method using geological modeling, the steps are as follows:

[0072] S521: Based on the guaranteed seismic data volume and in combination with reservoir well seismic calibration, the seismic reflection characteristics of the effective reservoir are identified and classified;

[0073] S522: Based on the optimization of seismic sensitive attributes and the carving threshold value test, the three-dimensional geometric morphology of seismic facies of different reservoir types is carved out;

[0074] S523: Under the guidance of geological modeling ideas, the three-dimensional geometric morphology of seismic phases of different reservoir types is transformed into a three-dimensional geometric structure model of fracture-cavity bodies;

[0075] S524: Conduct well-seismic joint seismic inversion to obtain seismic wave impedance constraint modeling volume;

[0076] S525: Obtaining a geological model of effective porosity of fracture-cavity bodies;

[0077] S526: Based on the result of the reserve calculation unit division, the area, effective thickness and effective porosity of different reservoir spaces in the reserve calculation unit are calculated by using the classified reservoir effective porosity body, and a map is compiled;

[0078] S6: Reserves estimation.

[0079] As a preferred method, in S12, the base value analysis method is used to correct the logging curves with stable formation thickness and clear mudstone standard layers; the probability statistics method and regression relationship method are used for those with unclear seismic characteristics and fast reservoir changes; and the dewiring smoothing filter method is used for abnormal values ​​of individual wells.

[0080] Preferably, the energy levels of the reflection structure in S24 are: parallel structure - low energy, sub-parallel-random hillock structure - low energy to high energy, divergent structure - high energy, chaotic structure - high energy to extremely high energy, non-reflective structure - low energy to extremely high energy, and the energy levels of the phase unit shapes are: mat-shaped - from low energy to high energy, wedge-shaped - from low energy to high energy, hillock-shaped - high energy, filling - low energy or high energy, lens-shaped - high energy.

[0081] As a preferred method, the phase-controlled random optimization inversion in S4 starts from the well-constrained geological model and uses the improved random hill climbing method for global optimization, so that the seismic data synthesized by the modified geological model forward model and the actual observed seismic data can achieve the best match. The improved random hill climbing method can perform sparse point search at high temperature and search in the current model at low temperature each time the model is iterated and updated.

[0082] Preferably, the method for calculating the effective porosity body of the classified reservoir in S514 is: merging the classified seismic phase geometric morphology carving results and the corresponding seismic porosity body and obtaining the intersection.

[0083] Preferably, the map preparation in S515 also requires using the classified reservoir effective porosity body to obtain the area, effective thickness and effective porosity of different reservoir spaces in the reserve calculation unit.

[0084] Preferably, S525 obtains a geological model of effective porosity of the fracture-cavity body based on single well logging modeling and seismic wave impedance constraint modeling combined with a three-dimensional geometric structure model of the fracture-cavity body.

[0085] Preferably, the reserve estimation in S6 adopts the fracture-cavity carving volumetric method.

[0086] The embodiments of the present invention are described in detail above, but the present invention is not limited to the above embodiments, and various changes can be made within the knowledge scope of those skilled in the art without departing from the spirit of the present invention.

Claims

1. A method for oil and gas exploration based on fracture-cavity identification; characterized in that: The steps are as follows: S1: well seismic calibration; S11: Seismic data reprocessing, including denoising, surface consistent amplitude compensation and random denoising; S12: Correcting the acoustic logging curve; S13: Real depth conversion and reflection coefficient calculation S131: The formula for real depth conversion using the integral of the acoustic time difference curve is: Where H0 is the starting depth in m, H is the ending depth in m, T0 is the time corresponding to the starting depth in s, T h is the time corresponding to the depth H, in seconds, Δt(h) is the acoustic time difference corresponding to the depth, in ms; S132: The difference in wave impedance between the upper and lower layers determines the formation transmission coefficient, which is expressed as: where ρ i 、V i is the density and velocity of the i-th layer on the logging curve, ρ i+1 and V i+1 is the density and velocity of the i+1th layer, ρ and V are derived from the velocity and density logging curves; S2: Seismic phase analysis S21: Search for the foreset reflection structure; S22: Classification of non-progradational structures, including parallel structures, subparallel-random hillock structures, divergent structures, chaotic structures and non-reflective structures; S23: Determine the spatial morphology of the reflection structure, including: mat-shaped, wedge-shaped, hillock-shaped, filling and lens-shaped; S24: Rationality analysis of the combination of reflection structure and appearance, the combination of the same phase unit, the principle of structure and appearance combination analysis along the strike and dip is: the energy level must match, that is, the reflection structure and appearance of the unified sedimentary body must be at the same energy level; S25: Amplitude, frequency and continuity are combined to identify their characteristic changes and divide seismic phases; S3: Phased Modeling S31: extracting the phase boundary of each sedimentary microfacies from the sedimentary phase map, and assigning the phase boundary line a coordinate value consistent with the work area according to the coordinate ratio relationship with the actual work area; S32: Project the phase boundary to the corresponding small layer in the isochronous stratigraphic framework, assign a specific integer value to the grid delineated by the phase boundary in the small layer as its phase code, which is used to identify the microfacies, and different sedimentary microfacies are assigned different phase codes; S33: digitizing the sedimentary facies maps of different layers into the isochronous stratigraphic framework model according to the above steps, and finally forming a three-dimensional sedimentary facies model; S4: Phase-controlled random optimization inversion, based on seismic data, combined with sedimentary facies knowledge, with well logging data of known wells as constraints, uses random optimization algorithms to maximize the similarity between synthetic seismic records and actual seismic records for inversion prediction; S5: Fracture carving, the carving methods include: the method of combining seismic attribute volume with seismic inversion volume and the seismic fracture carving method of geological modeling; S51: Combine the seismic attribute volume and the seismic inversion volume to perform fracture and hole carving. The steps are as follows: S511: Based on the fidelity seismic data volume and combined with reservoir well seismic calibration, the seismic reflection characteristics of effective reservoirs are identified and classified; S512: Based on the optimization of seismic sensitive attributes and the carving threshold value test, the three-dimensional geometric morphology of seismic facies of different reservoir types is carved out; S513: Carry out joint well-seismic inversion to obtain the seismic effective porosity volume; S514: Calculate and classify the effective porosity volume of the reservoir; S515: Prepare maps based on the results of reserve calculation unit division; S52: Seismic fracture and cave carving method using geological modeling, the steps are as follows: S521: Based on the guaranteed seismic data volume and in combination with reservoir well seismic calibration, the seismic reflection characteristics of the effective reservoir are identified and classified; S522: Based on the optimization of seismic sensitive attributes and the carving threshold value test, the three-dimensional geometric morphology of seismic facies of different reservoir types is carved out; S523: Under the guidance of geological modeling ideas, the three-dimensional geometric morphology of seismic phases of different reservoir types is transformed into a three-dimensional geometric structure model of fracture-cavity bodies; S524: Conduct well-seismic joint seismic inversion to obtain seismic wave impedance constraint modeling volume; S525: Obtaining a geological model of effective porosity of fracture-cavity bodies; S526: Based on the result of the reserve calculation unit division, the area, effective thickness and effective porosity of different reservoir spaces in the reserve calculation unit are calculated by using the classified reservoir effective porosity body, and a map is compiled; S6: Reserves estimation.

2. The oil and gas exploration method based on fracture-cavity identification according to claim 1 is characterized in that: In S12, the base value analysis method is used to correct the logging curves for wells with stable formation thickness and clear mudstone standard layers; the probability statistics method and regression relationship method are used for wells with unclear seismic characteristics and fast-changing reservoirs; and the de-wiring smoothing filter method is used for abnormal values ​​of individual wells.

3. The oil and gas exploration method based on fracture-cavity identification according to claim 1 is characterized in that: The energy levels of the reflection structure in S24 are: parallel structure - low energy, sub-parallel-random hillock structure - low energy to high energy, divergent structure - high energy, chaotic structure - high energy to extremely high energy, non-reflective structure - low energy to extremely high energy. The energy levels of the phase unit shapes are: sheet-shaped - from low energy to high energy, wedge-shaped - from low energy to high energy, hillock-shaped - high energy, filling - low energy or high energy, lens-shaped - high energy.

4. The oil and gas exploration method based on fracture-cavity identification according to claim 1 is characterized in that: The phase-controlled random optimization inversion in S4 starts from the well-constrained geological model and uses the improved random hill climbing method to globally optimize, so that the seismic data synthesized by the modified geological model forward model and the actual observed seismic data can achieve the best match. The improved random hill climbing method can perform sparse point search at high temperature and search in the current model at low temperature each time the model is iterated.

5. The oil and gas exploration method based on fracture-cavity identification according to claim 1 is characterized in that: The method for calculating the effective porosity body of the classified reservoir in S514 is: merging the classified seismic phase geometric morphology carving results and the corresponding seismic porosity body and obtaining the intersection.

6. The oil and gas exploration method based on fracture-cavity identification according to claim 1 is characterized in that: In S515, the preparation of maps also requires the use of classified reservoir effective porosity bodies to obtain the area, effective thickness and effective porosity of different reservoir spaces within the reserve calculation unit.

7. The oil and gas exploration method based on fracture-cavity identification according to claim 1 is characterized in that: Based on single well logging modeling and seismic wave impedance constraint modeling, S525 combines the three-dimensional geometric structure model of the fracture-cavity body to obtain the effective porosity geological model of the fracture-cavity body.

8. The oil and gas exploration method based on fracture-cavity identification according to claim 1 is characterized in that: The reserve estimation of S6 adopts the fracture-cavity carving volumetric method.

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