CN111611673B - Modeling method for carbonate reservoir ancient underground river type reservoir - Google Patents
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Abstract
The invention provides a carbonate rock ancient underground river type reservoir modeling method. The method solves the problem of lack of basis for modeling the geometric dimension of the underground river through the measurement of characteristic parameters and the rule statistics of the outcrop underground river; simulating and finely representing complex geometric morphology and structural characteristics of the underground river through multipoint geostatistics; and the model is optimized and modeled through the constraint of production dynamic data, so that the conformity of the model and the production is improved. The geological model constructed by the method effectively represents the development form, scale size and structural mode of the ancient underground river, comprehensively reflects the knowledge of the underground river under the current data condition, further improves the model precision, and further provides a more reliable basis for numerical simulation and development scheme adjustment of the oil reservoir.
Description
Technical Field
The invention relates to the technical field of oil and gas field development, in particular to a modeling method for a carbonate reservoir ancient underground river type reservoir.
Background
The karst ancient underground river reservoir of the carbonate reservoir is an important reservoir type, the ancient underground river system is the core of a reservoir karst system, the ancient underground river system has various forming control factors and is influenced by underground water supply, fracture, diving surface and lithology difference, and erosion and transformation coexist, so that the underground river has the defects of complex shape distribution, complex structure and poor regularity. The plane is provided with a single pipeline and a multi-pipeline network system which are continuous or discontinuous and have various filling characteristics; the section is distributed in a multilayer or single-layer way, and the single underground river karst cave is characterized by being approximately circular, oval and the like.
At present, the modeling of the oil reservoirs mainly comprises discrete holes, seams and holes, and the research on the modeling of the oil reservoirs is less compared with that of a continuous underground river. The number of wells actually drilled in the ancient karst pipeline is small, hard data are lacked, and the variation function analysis difficulty is high, so that the traditional two-point geostatistics method is difficult to characterize complex geometric morphology and spatial structure.
The related studies mainly include:
the patent with publication number 107219553A discloses a method for predicting the filling degree of a dark river based on GR frequency division inversion by using seismic data, which comprises the steps of carrying out frequency division processing on original seismic data by using a Marr wavelet frequency division technology to obtain frequency division data bodies of different frequency bands, extracting frequency division attributes of the different frequency bands from the frequency division data bodies respectively, constructing a kernel function according to the relation between amplitudes and frequencies under different thicknesses of a reservoir, carrying out multiple learning by using a support vector machine, establishing a nonlinear mapping relation between the frequency division attributes and a well logging GR curve, synthesizing the nonlinear mapping relations between the frequency division attributes of the different frequency bands and the well logging curve together to obtain a GR frequency division inversion body, determining GR peak value distribution probabilities corresponding to dark river samples of different filling degrees according to a well logging interpretation result, and determining the filling degree of the dark river of the frequency division inversion body.
Ancient river channel types are divided by Luxin Biao nationality oil reservoir karst ancient river channel development characteristic description in Tahe oil field, Luxin Biao et al, oil experiment geology, No. 36, No. 3, page 268, No. 274, 2014, a set of method and technology for depicting and identifying the ancient river channels is established, and 6 main river channels are actually identified.
Zhang Juan et al (ancient underground river seam cave structural characteristics and control factors in West of Tahe oilfield, Zhang Juan et al, oil and gas geology and recovery ratio, 25 th 4 th, 33 th-39 th, 2018) systematically carve and identify ancient underground river in West of Tahe oilfield, and the seam cave structure on the plane is divided into 2 major categories of strip shape with continuous development and block shape with discontinuous development, and the seam cave filling type can be divided into 3 types of unfilled, sandy-argillaceous filling and cornerite filling from top to bottom in the longitudinal direction.
By taking the township basin hardhat region as an example, the township and the like, a block oil and gas field, 23 rd, 6 th, 782 rd, 787 th and 2016 of the volley system characteristic description and address modeling, a geological conceptual model of the underground river karst cave system is established for the reservoir according to logging, earthquake, production data and the like, and 3 earthquake reflection characteristics of the underground river karst cave identification are summarized: sheet reflections, clutter reflections, and weak reflections.
The patent with publication number 103077558A discloses a method for modeling a distribution model of a large karst cave reservoir body of a fractured-vuggy carbonate rock reservoir, which takes the control effect of a karst development mode on the distribution of the large karst cave reservoir body into consideration and strengthens the constraint of geological rules.
The patent publication No. 103077548A discloses a modeling method of a distribution model of an erosion cavern reservoir body of a fractured-vuggy carbonate reservoir, which considers the huge difference between the erosion cavern reservoir body and other types of reservoir bodies such as a large-scale cavern and the like in the spatial dimension and scale of the reservoir bodies, separately establishes an erosion cavern reservoir body model, simultaneously establishes a quantitative probability relation between the distance from the large-scale cavern and the development of the erosion cavern reservoir body, and objectively reflects the distribution rule of the erosion cavern reservoir body.
The method for modeling the reservoir body of the multi-scale karst phased carbonate fracture-vug type oil reservoir is provided by Huyang et al (a multi-scale karst phased carbonate fracture-vug type oil reservoir modeling method, Huyang et al, Petroleum institute, 35 nd page, 2 nd stage, 340 nd page, 346 nd page, 2014), namely, under the control of an ancient karst development mode, a two-step method is adopted for modeling according to the difference of the dimensions of the fracture-vug: step 1, establishing 4 single-type reservoir discrete distribution models, namely identifying large karst caves and large-scale fractures by using earthquakes, and establishing a discrete large karst cave model and a discrete large-scale fracture model by using a deterministic modeling method; under the control constraint of karst phase, a karst cave development probability body and an interwell fracture development probability body are used for establishing a corrosion cavity model and a small-scale discrete fracture model by adopting a random simulation multi-attribute collaborative simulation method. And secondly, fusing single type reservoir body models into a multi-scale discrete fracture-cavity reservoir body three-dimensional distribution model by adopting a homothetic condition assignment algorithm.
The research of the multi-class multi-scale modeling method of the fracture-cave carbonate reservoir, which takes the Ordovician reservoir in the four regions of the Tahe oil field as an example, the research of the multi-class multi-scale modeling method of the fracture-cave carbonate reservoir, the research of the four regions of the Tahe oil field, the research of the fracture-cave carbonate reservoir, the research of the geoscience front, the No. 19, the No. 2, pages 59 to 66, 2012) provides a basic idea that the fracture-cave carbonate reservoir should be modeled according to the multi-class multi-scale modeling of large-scale caves, erosion holes, large-scale cracks and small-scale cracks.
Liu Yu Ming et al (the "Tahe oil field ancient karst reservoir body three-dimensional modeling", Liu Yu Ming et al, Chinese university of Petroleum institute (Nature science edition), vol 36, No. 2, pp 34-38, 2012) proposes a "vertical zoning, plane zoning, fracture priority" ancient karst reservoir body three-dimensional spread modeling method.
Luxinrui et al (carbonate reservoir multi-scale fracture-cave body classification characterization-taking the Ordovician reservoir of the unit S80 of the Tahe oil field as an example ", Luxinrui et al, oil and gas geology, vol 38, No. 4, pp 813-821, 2017) take the river channel position encountered by well point drilling as hard data, combine with river channel description results, take seismic frequency division energy prediction attributes as simulation constraint conditions of the shape of underground river among wells, and adopt a deterministic modeling method to establish a branch river channel reservoir body distribution model.
The researches are all focused on underground river description and law recognition, the modeling process is constrained by adopting seismic attributes, a prediction amplification effect exists, the seismic prediction attributes are reflected as abnormal bodies, the prediction result is discontinuous, the width and thickness of the predicted river channel are more than one hundred meters, and the recognition difference with the scale (more within 20 m) of the field outcrop underground river is large.
In addition, there are a number of problems with the prior art, including: the modeling of the underground river shape is constrained by adopting a geophysical prediction result, an obvious amplification effect exists in earthquake prediction abnormal bodies, and the recognition difference between the width and thickness of the predicted river channel and the field outcrop scale is large; in the modeling process, the constraint of geological causes is lacked, the underground river model is mostly an external contour, and the characterization of underground river structural characteristics is lacked; the underground river attribute simulation is based on well point hard data, interpolation simulation is carried out among wells, and reliable constraint basis is lacked; these all cause the low reliability of the underground river geological model, and are difficult to provide reliable geological basis for the design and adjustment of such oil reservoir development schemes.
Disclosure of Invention
In order to solve the technical problems, the invention provides a multi-constraint ancient underground river geological modeling method, which integrates the construction of multi-information constraint models such as wells, earthquakes, field outcrop, production dynamics, comprehensive oil reservoir description results and the like, comprehensively reflects the knowledge of the underground river under the current data condition, and represents the development form, scale and structural mode of the underground river.
The invention discloses a modeling method for an ancient underground river type reservoir of a carbonate reservoir, and particularly relates to determination of ancient underground river characteristic parameters and distribution rules, training image manufacturing, determination of an ancient underground river modeling method, design of a multi-constraint method, a dynamic data optimization model, assignment and simulation of physical property parameters, and finally three-dimensional geological modeling of the ancient underground river type reservoir of the carbonate reservoir. The modeling method comprises the following steps:
a, preferably selecting an ancient karst underground river reservoir layer and a modern karst underground river outcrop which have similarity with the reservoir layer of a research area, observing the shape and the structure, measuring underground river characteristic parameters, counting related distribution rules, determining the real characteristic parameters of the ancient underground river and analyzing the rules;
b, classifying the underground rivers, and respectively making training images of different types of underground rivers for representing underground river characteristics under different conditions;
c, selecting a proper training image according to the type of the underground river in the research area, and simulating by adopting a multi-point geostatistics algorithm;
step D, integrating other prior recognitions such as karst cause laws, geophysical prediction results and the like in a probability body mode in the multi-point geostatistical simulation process to realize the multi-element constraint of the ancient and underground river reservoir modeling process;
e, determining physical property parameters of the single well, and establishing an underground river physical property parameter model by adopting a sequential Gaussian simulation method based on the ancient underground river spatial distribution model;
and F, optimizing the geological model based on the dynamic data.
Further, the step a mainly includes:
a1, analyzing the consistency of an outcrop area reservoir and a research oil reservoir, and determining an outcrop area with similarity;
step A2, determining parameters representing the characteristics of the underground river to form an underground river characteristic parameter library;
a3, analyzing the filling and collapsing distribution along the underground river, and drawing a corresponding river course spreading and structure plan;
and A4, analyzing the distribution rule of the relevant geometric characteristic parameters based on the established underground river characteristic parameter library, and using the analysis result to the constraint conditions of the inter-well river channel characteristic simulation.
Further, the step B includes:
b1, classifying the underground rivers from multiple angles to reflect the main characteristics of different types of underground rivers;
b2, respectively manufacturing training images of different types according to the classification of the underground rivers;
b3, correcting the width of the underground river;
step B4., constructing a training image pattern library of the underground river with different main control factors by using geological knowledge statistical information and combining modern karst investigation and research of underground river reservoirs in research areas from the aspects of main control factors of causes, vertical structures, plane shapes and the like.
Further, in the step B2, the creating a training image includes:
a. the method comprises the steps of integrating the modern underground river form with geometric parameters of the tower river outcrop ancient river channel, and considering the underground river collapse and burying effects to manufacture a three-dimensional training image; or
b. And D, constructing a training image by carving the seismic attributes of the dense well network area and combining the correction mode of the underground river mode in the step A.
Further, in the step B3, an accumulative probability curve method is adopted for the correction method of the ancient dark river width, and a corresponding accumulative frequency is found on the width accumulative frequency distribution curve of the similar outcrop area dark river pipeline for any point width; finding the ancient karst and underground river width with the same accumulated frequency on the ancient underground river karst width accumulated frequency curve, namely the corrected ancient underground river core corresponding width;
the influence range of the ancient underground river except the core part can be obtained by underground river pipeline physical collapse tests or field statistical rules, and the training image is corrected according to the relation between the core part and the influence range of the underground river.
Further, in the step C, the multi-point geological modeling algorithm includes an iterative method and a non-iterative method, and the iterative method is based on the idea of system optimization and is used for simulation of a relatively continuous target; non-iterative methods are image configuration based methods for simulation of features such as target structures.
Further, in the step C, the method further includes simulating the geometric form by matching the simulated target with the training image, and simulating the structural feature by matching the simulated target with the training image library, so as to reduce the uncertainty of the random simulation.
Further, the multivariate constraint of the modeling process in the step D includes:
step D1, restriction of karst cause;
step D2. constraints for other seismic attributes;
and D3, fusing the karst cause rule and the seismic attribute information probability body, wherein the fusing method comprises the following steps:
wherein: p { }, representing probability; c, indicating that karst cave occurs; k, representing karst cause information; and S, representing seismic attribute information.
Further, the step E mainly includes:
(1) for the part of the single well with the logging curve, obtaining the porosity and permeability parameters of the single well according to the interpretation of the logging curve;
(2) for the part of a single well without curves or disordered curves, carrying out parameter assignment by combining with the production dynamic characteristics;
(3) and (3) establishing an underground river physical property parameter model by using the single-well physical property parameter as hard data and using the ancient underground river spatial distribution model as a constraint condition and adopting a sequential Gaussian simulation method.
Further, in the step (2) in the step E, for a single well without curves or a part with curves mixed up, performing parameter assignment by combining production dynamic characteristics includes:
determining single well porosity information according to the single well accumulated production and initial production information;
and obtaining single well permeability data according to production well testing or production dynamic analysis inversion.
Further, the step F includes:
(1) optimizing a geological model based on dynamically judging the connectivity among wells;
for dynamically judged well groups communicated through the underground river, locally optimizing the discontinuity of the underground river karst cave through a target-based algorithm to ensure the connectivity of the underground river; or
(2) Optimizing the geological model based on the dynamic reserves;
and optimizing the well control static geological reserves by using the well control dynamic reserves data, optimizing the porosity or volume of individual well control karst caves in the model by using the single-well dynamic geological reserves as conditional data on the premise that the total volume of the fracture-cave unit is not changed, and enabling the well control reserves in the model to be consistent with the dynamic production data.
Further, in the step F, in optimizing the geological model based on the dynamic reserve, the optimization method of the geological model is an annealing simulation method, and includes:
(1) constructing an objective function
Setting a reasonable error range;
(2) carrying out numerical simulation on the basis of the initial model to judge production dynamic information and judge the error magnitude of a target function value;
(3) if the error range is less than or equal to the reasonable error range, judging as a reasonable model; if the error range is larger than the reasonable error range, continuing optimization until the error reaches the set reasonable range;
wherein,
-represents the target error, which continues the optimization when it is greater than 5%, and terminates the optimization when it is less than or equal to 5%; o is d Represents a dynamically calculated well control reserve; o is s Representing well control reserves calculated based on a geological model.
Compared with the prior art, the carbonate reservoir ancient underground river type reservoir modeling method solves the problem that the underground river geometric dimension modeling lacks basis through outcrop underground river characteristic parameter measurement and regular statistics, and aims at solving the problems that the ancient underground river structural characteristics are difficult to effectively characterize by the traditional method, the reliable constraint basis is lacked, and the model reliability is low, and the construction of a comprehensive well, earthquake, field outcrop, production dynamics, reservoir comprehensive description achievement and other multivariate information constraint models is integrated; the complex geometric morphology and structural characteristics of the underground river are represented finely through multi-point geostatistics simulation, and the model and production conformity degree is improved and the model precision is further improved through production dynamic data constraint optimization modeling.
The technical features described above can be combined in various technically feasible ways to produce new embodiments, as long as the object of the invention is achieved.
Drawings
The invention will be described in more detail hereinafter on the basis of non-limiting examples only and with reference to the accompanying drawings. Wherein:
FIG. 1 shows a block diagram of the steps of a method for modeling a carbonate reservoir ancient underground river type reservoir according to the present invention;
fig. 2 shows a schematic view of the open-head underground river spread and structural features in embodiment 1 of the present invention;
FIG. 3 is a schematic view showing a single-pipe type underground river in example 1 of the present invention, the arrows indicating the direction of water flow;
FIG. 4 is a schematic view showing a pipe network type underground river in example 1 of the present invention, arrows indicating the direction of water flow;
fig. 5 is a schematic view showing a broken gallery type underground river in example 1 of the present invention, arrows representing the directions of water flow;
fig. 6 shows a typical single-branch pipe type underground river training image in embodiment 1 of the present invention, which characterizes the shape, geometric size, water flow direction, filling characteristics, structural characteristics, combination relationship thereof, and the like of the underground river;
fig. 7 shows a typical training image of the ancient underground river pipeline network in embodiment 1 of the present invention, which represents the form, geometric size, water flow direction, structure, etc. of the pipeline network;
FIG. 8 is a schematic view showing the correction of the influence range width of the ancient underground river nucleus in example 1 of the present invention;
FIG. 9 is a schematic view showing the correction of the influence ranges of the ancient underground rivers except the nuclear part in example 1 of the present invention;
FIG. 10 shows the development probability of a river under different karst ancient landforms (plateau, slope, depression) and different karst zones (surface, seepage, runoff) in example 1 of the present invention;
FIG. 11 shows the results of modeling the upper river of the R1 reservoir in example 2 of the present invention;
FIG. 12 shows the R1 reservoir lower river modeling results in example 2 of the present invention;
FIG. 13 shows the modeling results of the R1 reservoir channel as a whole in example 2 of the present invention;
FIG. 14 is a graph showing the results of daily oil production simulation of R1 reservoir in comparison with actual production in example 2 of the present invention;
FIG. 15 is a graph showing the comparison of the daily water cut simulation results of R1 reservoir with the actual water cut in example 2 of the present invention;
fig. 16 is a width distribution diagram showing the river channel modeling result before correction in example 3 of the present invention;
fig. 17 is a width distribution diagram showing the corrected river modeling result in example 3 of the present invention;
fig. 18 shows a single river anomaly based on the underground river model established by seismic sculpturing in example 3 of the present invention;
fig. 19 shows a river model established based on the method in embodiment 3 of the present invention, which characterizes the structural features of the river;
FIG. 20 shows the ancient inland river porosity model before optimization in example 4 of the present invention;
FIG. 21 shows the optimized model of porosity of the ancient underground river in example 4 of the present invention;
figure 22 shows the well control reserve size versus dynamic reserve for three wells before and after optimization in example 4 of the present invention.
Detailed Description
The invention will be described in further detail below with reference to the drawings and specific examples. It should be noted that, as long as there is no conflict, the embodiments and the features of the embodiments of the present invention may be combined with each other, and the technical solutions formed are within the scope of the present invention.
Example 1
As shown in fig. 1, the modeling method of the carbonate reservoir ancient underground river type reservoir comprises the following steps:
and A, preferably selecting an ancient karst underground river reservoir layer and a modern karst underground river outcrop which have similarity with the reservoir layer in the research area, observing the shape and the structure, measuring underground river characteristic parameters, counting related distribution rules, determining the real characteristic parameters of the ancient underground river and analyzing the rules. The method specifically comprises the following steps:
step A1, determining an outcrop area with similarity: and analyzing the consistency of the reservoir of the modern karst underground river outcrop area and the aspects of researching the ancient climate, the ancient geography, the ancient water system environment, the soluble rock components, the underground river form, the structural mode and the like of the oil reservoir, and taking the area with the similarity of more than 80 percent as a similar outcrop area.
Step A2, determining parameters for characterizing the characteristics of the underground river, wherein the parameters for characterizing the characteristics of the underground river mainly comprise 3 types: the structure type of the underground river (main river, branch river, hall cave, etc.), geometric parameters (length, width, thickness, length-width ratio, width-thickness ratio) and internal characteristics (filling, filling degree, physical properties), etc.
And (3) counting the structure and the parameter list of the typical outcrop underground river shown in the table 1, and integrating a plurality of outcrop and actual drilling underground river parameters to form an underground river characteristic parameter library so as to provide a basis for the actual geometric dimension of the river channel.
TABLE 1 typical outcrop underground river structure and parameter List
And A3, analyzing the filling and collapsing distribution along the underground river, drawing a corresponding river course spreading and structural plan as shown in figure 2, and providing a foundation for analyzing and simulating the structural characteristics of the river course.
And A4, analyzing the distribution rule of the relevant geometric characteristic parameters based on the established underground river characteristic parameter library, and using the analysis result to the constraint conditions of the inter-well river channel characteristic simulation.
And step B, classifying the underground rivers, and respectively making training images of different types of underground rivers for representing the characteristics of the underground rivers under different conditions. The method specifically comprises the following steps:
b1, classifying underground rivers;
the method can be classified from a plurality of angles, and aims to reflect the main characteristics of different types of underground rivers so as to be expressed through training images. For example, according to the difference of geometrical characteristics of the underground river system, the underground river types can be divided into 3 classes of single branch pipelines, pipeline networks and broken corridor pipelines,
firstly, as shown in fig. 3, a schematic diagram of a single-branch pipeline type underground river is shown, a pipeline tunnel main body is approximately circular, the underground river is linearly distributed, the diameter of a main tunnel body is 2-10 m, the length of the main tunnel body is 1-30 km, only an underground river pipeline with a main water inlet and a main drainage outlet is developed, and a section structure is formed by the pipeline main body and a peripheral influence zone of the tunnel body.
Secondly, as shown in fig. 4, the underground river is a schematic view of a pipeline network type underground river, the main body of the underground river pipeline tunnel is approximately round or oval, the underground river is distributed in a net shape, the diameter of the main tunnel body is 2-10 m, and the length of the main tunnel body is 10-hundreds of kilometers; the main pipeline and the corrosion influence zone around the main pipeline are distributed in a branch shape, the main pipeline and at least 1 branch pipeline form, and the karst forms such as a water falling hole, a vertical shaft and the like develop in the middle.
Thirdly, as shown in fig. 5, the underground river pipeline is a schematic diagram of a broken corridor type underground river, the height-to-width ratio of the underground river pipeline is larger than 5, the underground river pipeline generally extends along a broken structural zone in a zigzag mode on a plane, the vertical space is large, the section width is 1-10 m, the height is 5-10 m, and the length is 0.5-5 km.
B2, making a training image;
a. the method integrates the modern underground river form with the geometric parameters of the tower river outcrop ancient river channel, and simultaneously considers the underground river collapse and burial effect to manufacture a three-dimensional training image. Or
b. And D, constructing a training image by carving the seismic attributes of the dense well network area and combining the correction mode of the underground river mode in the step A.
The training image can reflect the combination relation of the geometric form, the size parameter, the spreading rule and the structural feature of the underground river on three dimensions.
Fig. 6 shows a typical single-branch pipe type underground river training image, and fig. 7 shows a typical pipe network training image, which respectively represents the geometric shapes, sizes, water flow directions, filling features, structural features, combination relationships thereof, and the like of a single-branch pipe type underground river and a pipe network.
B3, correcting the width of the underground river;
because the modern river width and the probability distribution thereof have larger access to the tower river outcrop ancient underground river karst cave width, the modern karst cave width data needs to be corrected; as shown in FIG. 8, the correction of the width of the underground river can adopt a cumulative probability curve method, and for any width (such as 82 meters) of a modern karst cave, a corresponding cumulative frequency (55%) is found on a cumulative frequency distribution curve of the width; finding the ancient karst and underground river width (3.7 meters) with the same cumulative frequency (55 percent) on the ancient and underground river karst cave width cumulative frequency curve, namely the corrected ancient and underground river core part width, correcting all underground river widths to form an underground river width correction table, and correcting the widths according to the width correction table. The corrected karst cave width is not only consistent with the scale of the ancient karst cave, but also completely consistent with the mathematical distribution.
In addition, the training image obtained in the step B2 a can be corrected by the method, and the corrected result is consistent with the characteristics of the underground river observed in the similar outdoor head area.
Step B4. training image library construction;
by utilizing geological knowledge statistical information and combining modern karst investigation and underground river reservoir research in a research area, a training image pattern library of various underground rivers with different main control factors is constructed in consideration of main control factors, vertical structures, plane shapes and the like.
The main control factors of the cause are divided into fault main control, diving surface main control, hydrodynamic force-crack main control and the like; the vertical structural factors are divided into single-layer underground river karst caves, multi-layer underground river karst caves and the like; the planar form factors are divided into single river channels, reticular river channels and the like. The spreading rule, the geometric shape, the river channel structure and the like of various river channels are different.
And selecting a proper training image according to the characteristics of the modeling object, so that the aim of accurate modeling can be fulfilled. Fig. 9 shows a corrected underground river reservoir body diagram, in which the influence range of the ancient underground river except the nuclear part can be clearly seen.
And C, selecting a proper training image according to the type of the underground river in the research area, and simulating by adopting a multi-point geostatistics algorithm.
(1) The multi-point geological modeling algorithm can select an iterative method and a non-iterative method. The iterative method is based on the idea of system optimization and is used for simulation of relative continuous targets; non-iterative methods are image configuration based methods for simulation of features such as target structures.
(2) The geometric form is simulated by adopting a mode of matching the simulated target with the training image, the structural characteristics are simulated by adopting a method of matching the simulated target with the training image library, and the uncertainty of random simulation is reduced.
(3) According to the research object, a single-layer underground river, a multi-layer underground river and the like in the simulated reservoir can be selected.
And D, in the multipoint geostatistics simulation process, integrating karst cause laws, geophysical prediction results and other prior knowledge in a probability body mode to improve the coincidence rate of the model and actual knowledge, and realizing a multivariate constraint method. The method specifically comprises the following steps:
step D1, restriction of karst cause law;
for example, statistics of the development probabilities of the underground river under different karst ancient landforms (highland, slope and depression) and different karst zones (surface layer, seepage and runoff) as shown in fig. 10 are taken as a cooperative constraint condition in the simulation process, so that the control of the cause rule of the underground river is embodied.
Step D2. constraints for other seismic attributes;
for the seismic attributes which do not participate in modeling in the previous steps, the response relation between the single-well seismic attributes and the underground river karst cave frequency can be counted, seismic attribute data are converted into an underground river karst cave development probability body, the underground river karst cave development probability body is used as a collaborative constraint condition of modeling, and the control effect of different seismic attributes is reflected.
And D3, the karst cause rule and the seismic attribute information probability body are different and overlapped, and the underground river karst cave development comprehensive probability body is obtained by fusing the probability bodies formed by the karst cause rule and the seismic attribute information probability body. The fusion method comprises the following steps:
wherein: p { }, representing a probability; c, indicating that karst cave occurs; k, representing karst cause information; and S, representing seismic attribute information.
And E, determining physical property parameters of the single well by using drilling, logging and production dynamic inversion interpretation data, and establishing an underground river physical property parameter model by adopting a sequential Gaussian simulation method based on the ancient underground river spatial distribution model.
(1) And for the part with the logging curve of the single well, obtaining the porosity and permeability parameters of the single well according to the interpretation of the logging curve.
(2) For a single well part without curves or mixed and disorderly curves, parameter assignment is carried out by combining production dynamic characteristics, specifically: determining the porosity information of the single well according to the cumulative production and initial production information of the single well; and obtaining single well permeability data according to production well testing or production dynamic analysis inversion.
(3) And (3) establishing an underground river physical property parameter model by using the single-well physical property parameter as hard data and using an ancient underground river spatial distribution model as a constraint condition and adopting a sequential Gaussian simulation method.
And F, optimizing the geological model based on the dynamic data.
In the modeling process, the ancient underground river is well matched with a single well, seismic data and geological rules, but the model is not matched with dynamic data, such as connectivity, well control reserves and the like. The method comprises the following steps:
(1) and optimizing the geological model based on dynamic judgment of the connectivity among wells. And for the dynamically judged well group communicated through the underground river, locally optimizing the discontinuity of the underground river karst cave through a target-based algorithm, and ensuring the connectivity of the underground river.
(2) The geological model is optimized based on the dynamic reserves. And optimizing the porosity or volume of the individual well control karst cave in the model by using the well control dynamic reserve data to optimize the well control static geological reserve and taking the single well dynamic geological reserve as conditional data on the premise that the total volume of the fracture-cave unit is not changed, so that the well control reserve in the model is consistent with the dynamic production data. The adopted optimization method can adopt an annealing simulation method to construct an objective function
Wherein:
-represents the target error, which continues the optimization when it is greater than 5%, and terminates the optimization when it is less than or equal to 5%; o is d Represents a dynamically calculated well control reserve; o is s Representing well control reserves calculated based on a geological model.
According to the modeling method of the carbonate reservoir ancient underground river type reservoir provided by the invention, the R1 reservoir, the R2 reservoir and the R3 reservoir are taken as examples and are implemented on site.
Example 2
The R1 reservoir is a typical carbonate rock ancient underground river type reservoir and is provided with an upper river channel and a lower river channel, wherein the upper river channel is a typical single-branch pipeline type underground river, and the lower river channel is a typical pipeline network underground river.
According to the method, similar outcrops are optimized, relevant parameters shown in table 1 are measured, multi-disciplinary data are integrated to construct a training image, and fig. 6 is a typical single-branch pipeline type underground river training image of an R1 oil reservoir, and the underground river form, the geometric dimension, the water flow direction, the filling characteristic, the structural characteristic, the combination relation of the structural characteristic and the like are represented. Fig. 7 is a typical ancient underground river pipeline network training image of an R1 oil reservoir, which represents the form, geometric dimension, water flow direction, structural characteristics and the like of the pipeline network. FIG. 10 shows the development probability of the river under different karst paleography (plateau, slope, depression) and different karst zones (surface layer, seepage and runoff) of the R1 reservoir. FIG. 11 is a modeling result of the R1 reservoir upper river according to the method. FIG. 12 is a modeling result of R1 reservoir lower river according to the method. FIG. 13 shows the modeling results of the R1 reservoir channel as a whole. FIG. 14 is a graph comparing the simulation result of the daily oil production of R1 reservoir with the actual daily oil production, and it can be seen that the coincidence rate of the two is more than 95%. FIG. 15 is a comparison graph of the water cut simulation result of R1 reservoir day and the actual water cut, and the coincidence rate of the two reaches more than 90%. Compared with the actual production condition, the simulation result based on the model has the advantages that the coincidence rate is greatly improved, the distribution of the residual oil is clearer, and the accuracy and the efficiency of the model are reflected.
Example 3
The R2 oil reservoir is a typical carbonate rock ancient underground river reservoir of a certain oil field, 56 mouths of wells are drilled, the underground river flows from the north to the south, the total length is about 19 kilometers, and the whole underground river grows 10-250 meters below the unconformity surface.
The geologic model constructed by the method has the advantages that the first layer of underground river grows 10-100m below the unconformity surface, the north part is a single underground river, and the south part is a net-shaped river; the second layer of underground river grows 100-250m below the unconformity, the number of branch river channels is less, and the coverage range is limited. Through verifying 10 diluted pumping wells, the drilling coincidence rate of the diluted pumping wells and the actual drilling wells is improved from 67% to 93%. The river channel width is corrected to 10-50 m from 30-170 m of original seismic sculpture, and is more consistent with the underground river characteristics observed in similar outcrop areas in the field, the width distribution diagram of the river channel modeling result before correction is shown in fig. 16, and the width distribution diagram of the river channel after correction is shown in fig. 17. The characterization accuracy of the model is from a single river channel abnormal body (fig. 18 is a river channel model established based on seismic carving and is a single river channel abnormal body) to the modeling of the river channel structure (fig. 19 is a river channel model established based on the method and characterizes the structural characteristics of the river channel), the new model comprises 2 layers of main river channels, 6 branch river channels, 5 erosion zones and water falling caves, the complex structure is characterized, and the model accuracy is improved.
Example 4
The R3 oil reservoir is a typical carbonate rock ancient underground river reservoir of a certain oil field, a three-dimensional geological model of the ancient underground river reservoir is established based on the method, a porosity model of the ancient underground river is shown in figure 17, a geological model based on dynamic reserve optimization is shown in figure 18, and a comparison between the three typical wells before and after well control reserve optimization and the dynamic reserve is shown in figure 19, so that the coincidence rate of the well control reserve and the dynamic reserve after optimization is obviously increased, and the model precision is improved.
Compared with the conventional method, the geological model constructed by the method effectively represents the development form, scale size and structural mode of the ancient underground river, comprehensively reflects the knowledge of the underground river under the current data condition, further improves the model precision, and further provides a more reliable basis for numerical reservoir simulation and development scheme adjustment.
Although the embodiments of the present invention have been described above, the above descriptions are only for the convenience of understanding the present invention, and are not intended to limit the present invention. It will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (10)
1. A modeling method for an ancient underground river type reservoir of a carbonate reservoir is characterized by comprising the following steps:
a, preferably selecting an ancient karst underground river reservoir layer and a modern karst underground river outcrop which have similarity with the reservoir layer of a research area, observing the shape and the structure, measuring underground river characteristic parameters, counting related distribution rules, and determining the real characteristic parameters and the distribution rule characteristics of the ancient underground river;
b, classifying the underground rivers, and respectively making training images of different types of underground rivers for representing underground river characteristics under different conditions;
c, selecting a suitable training image according to the type of the underground river in the research area, and simulating by adopting a multi-point geostatistics algorithm;
step D, integrating prior knowledge including karst cause laws and geophysical prediction results in a probability body mode in the multi-point geostatistical simulation process to realize a multi-element constraint method of the ancient underground river reservoir modeling process;
e, determining physical property parameters of the single well, and establishing an underground river physical property parameter model by adopting a sequential Gaussian simulation method based on the ancient underground river spatial distribution model;
and F, optimizing the geological model based on the dynamic data.
2. The method for modeling a carbonate reservoir ancient underground river type reservoir according to claim 1, wherein the step a mainly comprises:
a1, analyzing the consistency of an outcrop area reservoir and a research oil reservoir, and determining an outcrop area with similarity;
step A2, determining parameters representing the characteristics of the underground river to form an underground river characteristic parameter library;
a3, analyzing the filling and collapsing distribution along the underground river, and drawing a corresponding river course spreading and structure plan;
and A4, analyzing a distribution rule of the relevant geometric characteristic parameters based on the established underground river characteristic parameter library so as to be used for constraint conditions of inter-well river channel characteristic simulation.
3. The modeling method for the carbonate reservoir ancient underground river type reservoir according to claim 2, wherein the step B comprises:
b1, classifying the underground rivers from multiple angles to reflect the main characteristics of different types of underground rivers;
b2, respectively manufacturing training images of different types according to the classification of the underground rivers;
b3, correcting the width of the underground river;
step B4., constructing a training image pattern library of the underground river with different main control factors by using geological knowledge statistical information and combining modern karst investigation and research of underground river reservoirs in a research area in consideration of main control factors, vertical structures and plane forms of causes; wherein, the main control factors of the cause comprise fault main control, diving surface main control and hydrodynamic-crack main control; the vertical structural factors comprise a single-layer underground river karst cave and a multi-layer underground river karst cave; the plane form factors comprise single river channels and reticular river channels.
4. The modeling method for the ancient underground river type reservoir of carbonate reservoir according to claim 3, wherein in the step B2, the making of the training image comprises:
a. the method comprises the steps of integrating the modern underground river form with the geometric parameters of the tower river outcrop ancient river channel, and meanwhile considering the underground river collapse and burial effect to manufacture a three-dimensional training image; or
b. And B, constructing a training image by carving the seismic attributes of the dense well network area and combining the correction mode of the underground river mode in the step A.
5. The method for modeling a carbonate reservoir ancient underground river type reservoir according to claim 4, wherein in the step B3,
the method for correcting the ancient underground river width adopts an accumulated probability curve method, and corresponding accumulated frequency is found on a width accumulated frequency distribution curve of the similar outcrop area underground river aiming at the width of any point; finding the ancient karst and underground river width with the same accumulated frequency on the ancient underground river karst width accumulated frequency curve, wherein the ancient karst and underground river width is the width corresponding to the corrected ancient underground river core part;
and obtaining the influence range of the ancient underground river except the core part through an underground river pipeline physical collapse test or a field statistical rule, and correcting the training image according to the relation between the core part and the influence range of the underground river.
6. The modeling method for the carbonate reservoir ancient underground river type reservoir according to claim 1, wherein in the step C, the multi-point geological modeling algorithm comprises an iterative method and a non-iterative method, and the iterative method is based on the idea of system optimization and is used for simulation of relative continuous targets; the non-iterative method is an image configuration based method for simulation of features such as target structures.
7. The modeling method for the carbonate reservoir ancient underground river type reservoir according to claim 6, wherein in the step C, the geometric morphology is simulated by adopting a method of matching a simulation target with a training image, and the structural characteristics are simulated by adopting a method of matching a simulation target with a training image library, so that the uncertainty of random simulation is reduced.
8. The method for modeling a carbonate reservoir ancient underground river type reservoir according to claim 7, wherein the multivariate constraints of the modeling process in the step D comprise:
step D1, restriction of karst cause law;
step D2. constraints for other seismic attributes;
and D3, fusing the underground river karst cave development comprehensive probability body by a karst cause rule and an earthquake attribute information probability body, wherein the fusion method comprises the following steps:
wherein: p { }, representing probability; c, indicating that karst cave occurs; k, representing karst cause information; and S, representing seismic attribute information.
9. The modeling method for the carbonate reservoir ancient underground river type reservoir according to claim 8, wherein the step E mainly comprises:
(1) for the part of the single well with the logging curve, obtaining the porosity and permeability parameters of the single well according to the interpretation of the logging curve;
(2) for the part of a single well without curves or disordered curves, carrying out parameter assignment by combining with the production dynamic characteristics;
(3) and (3) establishing an underground river physical property parameter model by using the single-well physical property parameter as hard data and using the ancient underground river spatial distribution model as a constraint condition and adopting a sequential Gaussian simulation method.
10. The method of modeling a carbonate reservoir ancient inland river-type reservoir as claimed in claim 9, wherein said step F comprises:
(1) optimizing a geological model based on dynamically judging the connectivity among wells;
for dynamically judged well groups communicated through the underground river, locally optimizing the discontinuity of the underground river karst cave through a target-based algorithm to ensure the connectivity of the underground river; or
(2) Optimizing the geological model based on the dynamic reserves;
and optimizing the porosity or volume of the individual well control karst caves in the model on the premise that the total volume of the fracture-cave unit is not changed by using the well control dynamic reserves data to optimize the well control static reserves, taking the single well dynamic reserves as condition data, so that the well control reserves in the model are consistent with the dynamic production data.
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