CN109358364B

CN109358364B - Method, device and system for establishing underground river reservoir body geological model

Info

Publication number: CN109358364B
Application number: CN201811265621.7A
Authority: CN
Inventors: 宋随宏; 侯加根; 康强强; 蒋超; 刘钰铭
Original assignee: China University of Petroleum Beijing
Current assignee: China University of Petroleum Beijing
Priority date: 2018-10-29
Filing date: 2018-10-29
Publication date: 2020-05-15
Anticipated expiration: 2038-10-29
Also published as: CN109358364A

Abstract

The embodiment of the specification discloses a method, a device and a system for establishing a geological model of an underground river reservoir body, wherein the method comprises the steps of obtaining an underground river training image and logging interpretation data of a target work area; determining comprehensive development probability data of underground river reservoir bodies according to the seismic attribute data and the karst ancient landform data of the target work area; and under the constraint of the comprehensive development probability data of the underground river reservoir body, establishing an underground river reservoir body geological model of the target work area by using the logging interpretation data and the underground river training image as input data and utilizing a multi-point geostatistical simulation algorithm. By utilizing the embodiments of the specification, the three-dimensional spatial distribution of the constructed underground river reservoir body can be more consistent with geological rules.

Description

Method, device and system for establishing underground river reservoir body geological model

Technical Field

The invention relates to the technical field of petroleum and natural gas exploration and development, in particular to a method, a device and a system for establishing a geological model of an underground river reservoir body.

Background

Underground river is a river below the ground, mainly developed in limestone and developed in both modern and ancient times. Underground rivers which develop in geological history period (such as 4 hundred million years ago) are likely to be filled with sand, mud or gravel, and then buried by later deposition and buried underground (such as 400 meters underground) to become ancient underground rivers. When oil and gas are transported into underground rivers, the entire underground river is called an underground river reservoir.

In a karst cave type carbonate oil reservoir, the underground river reservoir body greatly contributes to the productivity, so that the underground river reservoir body is of great importance to the distribution prediction and three-dimensional geological modeling of the ancient underground river reservoir body. In the aspect of underground river reservoir modeling technology, methods such as sequential indication simulation under the constraints of earthquake and geological conditions, and seismic attribute direct truncation under the control of karst cause modes and the like have been proposed at present. Although the methods achieve certain effects in the aspect of carbonate rock underground river modeling, the methods are provided for large-range distributed clastic rocks, and the integration strength of various data in the modeling process is insufficient, so that the continuity and the form of the underground river reservoir body obtained through simulation are influenced, and the difference from the actual geological rule is large.

Therefore, a method for more accurately constructing a three-dimensional geological model of an underground river reservoir body is needed in the technical field.

Disclosure of Invention

The embodiment of the specification aims to provide a method, a device and a system for establishing a geological model of an underground river reservoir body, so that the three-dimensional spatial distribution of the underground river reservoir body obtained by construction can better accord with geological rules.

The specification provides a method, a device and a system for establishing a geological model of an underground river reservoir body, which are realized by the following modes:

a method of creating a subsurface river reservoir geological model, comprising:

acquiring underground river training images and well logging interpretation data of a target work area;

determining comprehensive development probability data of underground river reservoir bodies according to the seismic attribute data and the karst ancient landform data of the target work area;

and under the constraint of the comprehensive development probability data of the underground river reservoir body, establishing an underground river reservoir body geological model of the target work area by using the logging interpretation data and the underground river training image as input data and utilizing a multi-point geostatistical simulation algorithm.

In another embodiment of the method provided in this specification, the acquiring a training image of an underground river of a target work area includes:

constructing an initial underground river training image of a target work area by using actually measured underground river data of a surface karst area similar to the target work area;

and correcting the diameter of the underground river in the initial underground river training image by using outcrop diameter data of the underground river karst cave of the target work area to obtain the underground river training image of the target work area.

constructing an initial underground river training image of a target work area by using seismic attribute data of the target work area;

In another embodiment of the method provided in this specification, the correcting the diameter of the underground river in the initial underground river training image by using outcrop diameter data of the underground river cavern of the target work area includes:

acquiring outcrop diameter data of an underground river karst cave of a target work area, and calculating to obtain accumulated frequency data of the diameter of a first underground river according to the outcrop diameter data;

calculating to obtain second underground river diameter accumulated frequency data according to the underground river diameter data in the initial underground river training image;

and correcting the diameter of the underground river in the initial underground river training image according to the first underground river diameter accumulated frequency data and the second underground river diameter accumulated frequency data.

In another embodiment of the method provided in this specification, the determining comprehensive development probability data of the underground river reservoir includes:

obtaining a first functional relation between the development probability of the underground river reservoir body and the seismic attribute according to the logging interpretation data and the logging peripheral seismic attribute data in a statistical manner, and calculating to obtain first underground river reservoir body development probability data of a target work area according to the first functional relation and the seismic attribute data of the target work area;

determining a second functional relation between the development probability and the vertical depth of underground river reservoir bodies in different karst ancient landform facies zones according to the well logging interpretation data, and determining second underground river reservoir body development probability data of a target work area according to the second functional relation and corresponding karst ancient landform facies zones of the target work area;

and determining the comprehensive development probability data of the underground river reservoir body of the target work area according to the development probability data of the first underground river reservoir body and the development probability data of the second underground river reservoir body.

and fusing the first underground river reservoir body development probability data and the second underground river reservoir body development probability data according to a non-independent conditional probability fusion method to obtain comprehensive development probability data of the underground river reservoir body.

In another aspect, the present specification further provides an apparatus for creating a geologic model of a subsurface river reservoir, the apparatus including:

the data acquisition module is used for acquiring underground river training images and well logging interpretation data of the target work area;

the development probability determining module is used for determining comprehensive development probability data of the underground river reservoir body according to the seismic attribute data of the target work area and the karst ancient landform data;

and the geological model building module is used for building the geological model of the underground river reservoir body of the target work area by using the logging interpretation data and the underground river training image as input data and utilizing a multi-point geostatistical simulation algorithm under the constraint of the comprehensive development probability data of the underground river reservoir body.

In another embodiment of the apparatus provided in the present specification, the developmental probability determination model includes:

the first development probability determining unit is used for obtaining a first functional relation between the development probability of the underground river reservoir body and the seismic attribute according to the logging interpretation data and the logging peripheral seismic attribute data in a statistical manner, and calculating and obtaining first underground river reservoir body development probability data of a target work area according to the first functional relation and the seismic attribute data of the target work area;

the second development probability determining unit is used for determining a second functional relationship between the development probability and the vertical depth of the underground river reservoir body in different karst ancient landform facies zones according to the logging interpretation data, and determining second underground river reservoir body development probability data of the target work area according to the second functional relationship and the corresponding karst ancient landform facies zones of the target work area;

and the comprehensive development probability determining unit is used for determining the comprehensive development probability data of the underground river reservoir body of the target work area according to the first underground river reservoir body development probability data and the second underground river reservoir body development probability data.

In another aspect, embodiments of the present specification further provide an apparatus for creating a subsurface river reservoir geological model, including a processor and a memory for storing processor-executable instructions, which when executed by the processor, implement steps including:

In another aspect, the present specification further provides a system for creating a geological model of a subsurface river reservoir, comprising at least one processor and a memory storing computer-executable instructions, wherein the processor executes the instructions to implement the steps of the method according to any one of the above embodiments.

According to the method, the device and the system for establishing the geological model of the underground river reservoir body, which are provided by one or more embodiments of the specification, the comprehensive development probability data of the underground river reservoir body can be determined according to the seismic attribute data and the karst ancient landform data of the target work area. And then, simulating the three-dimensional space distribution of the underground river reservoir body in the target work area by using a multi-point geostatistics algorithm by taking the underground river reservoir body development section drilled in the shaft as modeling hard data and comprehensive development probability data as constraint data and combining an underground river training image. By utilizing the embodiments of the specification, the underground river form and scale can be accurately and effectively simulated, the simulation result has good continuity, and the existing data and geological rules are highly matched.

Drawings

In order to more clearly illustrate the embodiments of the present specification or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, it is obvious that the drawings in the following description are only some embodiments described in the present specification, and for those skilled in the art, other drawings can be obtained according to the drawings without any creative effort. In the drawings:

FIG. 1 is a schematic flow chart diagram of an embodiment of a method for creating a geologic model of a subsurface river reservoir provided herein;

FIG. 2(a) is a schematic diagram of preferred seismic attributes in one embodiment provided herein;

FIG. 2(b) is a schematic illustration of an initial underground river training image resulting from processing of seismic attribute data in another embodiment provided herein;

fig. 3(a) is a schematic diagram of a plan layout of a subsurface river for a field survey of a similar karst area on the earth's surface in another embodiment provided herein;

fig. 3(b) is a schematic diagram of an initial underground river training image constructed based on underground river survey data of similar surface karst areas in another embodiment provided in the present specification;

FIG. 4(a) is a schematic view of an initial underground river training image in another embodiment provided herein;

fig. 4(b) is a cross-sectional view of a field outcrop of a target work area underground river cavern in another embodiment provided by the present specification;

FIG. 4(c) is a graph illustrating cumulative frequency curves of underground river diameters in an initial training image in another embodiment provided herein;

fig. 4(d) is a schematic diagram of an accumulated frequency curve of the underground river diameter determined according to the outcrop diameter data of the underground river in the target work area in another embodiment provided by the present specification;

FIG. 4(e) is a schematic diagram of a training image of a subterranean river in another embodiment provided herein;

FIG. 5 is a schematic diagram illustrating the relationship between the probability of developing a river versus the impedance profile of a seismic wave in another embodiment provided herein;

FIG. 6 is a schematic illustration of the probability of development of a subterranean river reservoir corresponding to different depths in different facies zones in another embodiment provided herein;

FIG. 7 is a schematic flow chart of the process of creating a three-dimensional geological model of a subsurface river reservoir in another embodiment provided herein;

fig. 8 is a schematic view of a construction process of an underground river training image in another embodiment provided in the present specification;

FIG. 9 is a schematic diagram of a training image of a subsurface river of the area constructed according to seismic attributes in another embodiment provided by the present specification;

FIG. 10 is a schematic flow chart of the PR-based method for constructing a multivariate constraint comprehensive probability body of underground river reservoir development in another embodiment provided in the present specification;

FIG. 11 is a seismic attribute constrained conditional probability mass diagram of subsurface river reservoir development in another embodiment provided herein;

FIG. 12 is a schematic illustration of a karst paleography constrained conditional probability volume for subsurface river reservoir development in another embodiment provided herein;

FIG. 13 is a schematic representation of an integrated probability mass for underground river reservoir development in another embodiment provided herein;

FIG. 14 is a schematic representation of a three-dimensional spatial distribution geological model of a subsurface river reservoir in another embodiment provided by the present specification;

fig. 15 is a schematic block diagram of an embodiment of an apparatus for creating a geologic model of a subsurface river reservoir provided by the present specification.

Detailed Description

In order to make those skilled in the art better understand the technical solutions in the present specification, the technical solutions in one or more embodiments of the present specification will be clearly and completely described below with reference to the drawings in one or more embodiments of the present specification, and it is obvious that the described embodiments are only a part of the embodiments of the specification, and not all embodiments. All other embodiments obtained by a person skilled in the art based on one or more embodiments of the present specification without making any creative effort shall fall within the protection scope of the embodiments of the present specification.

In a karst cave type carbonate oil reservoir, the underground river reservoir body greatly contributes to the productivity, so that the underground river reservoir body is of great importance to the distribution prediction and three-dimensional geological modeling of the ancient underground river reservoir body. In the aspect of underground river reservoir modeling technology, methods such as sequential indication simulation under the constraints of earthquake and geological conditions, and seismic attribute direct truncation under the control of karst cause modes and the like have been proposed at present. Although the methods achieve certain effects in the aspect of carbonate rock underground river modeling, the methods are provided for large-range distributed clastic rocks, and integration strength of various types of data in the modeling process is insufficient, so that the underground river reservoir obtained through simulation is poor in continuity, disordered in shape and large in difference with an actual geological rule.

Correspondingly, the embodiment of the specification provides a method for establishing a geological model of the underground river reservoir body, which can determine development probability data of the underground river reservoir body according to seismic attribute data and karst ancient landform data by acquiring an underground river training image and logging interpretation data of a target work area. And then, under the constraint of the development probability data of the underground river reservoir body, determining the three-dimensional space distribution of the underground river reservoir body of the target work area by utilizing a multipoint geostatistics random simulation algorithm according to the well logging interpretation data and the underground river training image. By utilizing the embodiments of the specification, data such as single well data, geological data and karst ancient landforms can be effectively integrated, so that the three-dimensional spatial distribution of the constructed underground river reservoir body is more consistent with geological rules.

Fig. 1 is a schematic flow chart of an embodiment of the method for establishing the geologic model of the underground river reservoir provided by the present specification. Although the present specification provides the method steps or apparatus structures as shown in the following examples or figures, more or less steps or modules may be included in the method or apparatus structures based on conventional or non-inventive efforts. In the case of steps or structures which do not logically have the necessary cause and effect relationship, the execution order of the steps or the block structure of the apparatus is not limited to the execution order or the block structure shown in the embodiments or the drawings of the present specification. When the described method or module structure is applied to a device, a server or an end product in practice, the method or module structure according to the embodiment or the figures may be executed sequentially or in parallel (for example, in a parallel processor or multi-thread processing environment, or even in an implementation environment including distributed processing and server clustering).

In one embodiment of the method for creating a geologic model of a subsurface river reservoir as illustrated in fig. 1, the method may comprise:

s2: and acquiring underground river training images and well logging interpretation data of the target work area.

The well logging interpretation data can be data, conclusion and the like obtained by analyzing and interpreting well logging actual measurement data. Typically, the hydrocarbons in a reservoir are pumped by drilling a well, and a plurality of wells may be drilled from one reservoir. After drilling and completing the well, a physical instrument can be put into the well to obtain physical characteristics in the oil deposit, such as logging data of resistivity, wave velocity and the like; the well log data is then interpreted into geological parameters such as lithology, porosity, etc. Furthermore, various types of logging data and geological parameters can be comprehensively interpreted to obtain logging interpretation conclusions, such as a development segment of a underground river reservoir body.

The training image may be an input data for a multi-point geostatistical simulation process. In a specific implementation, the training image may be a three-dimensional geological model, which reflects the features of the geologic body to be simulated, such as morphology, structure, scale, etc., and may be used to define the above-mentioned features of the geologic body generated by the simulation. The underground river training image can be a three-dimensional geological model of an underground river, and reflects the characteristics of the underground river in the work area to be simulated, such as form, structure, scale and the like. In some embodiments, the three-dimensional geological model of the underground river may be determined through seismic attributes, well log data, and the like, so as to obtain an underground river training image of the target work area, or an underground river distribution model determined by some prior art methods is used as the underground river training image in the embodiments of the present specification.

In one embodiment of the present description, seismic attributes may be preferred based on the degree of response to the underground river reservoir, and an initial underground river training image may be determined based on the preferred seismic attributes; and then, correcting the diameter of the underground river in the initial underground river training image by using outcrop diameter data of the underground river karst cave of the target work area to obtain the underground river training image of the target work area.

Seismic attributes that respond better to subsurface river reservoirs may be first preferred. The seismic data may refer to wave data that may reflect subsurface rocks, hydrocarbons, and other geological features. In general, seismic attributes may include a wide variety of attributes, such as amplitude, frequency, phase, energy, waveform, wave impedance, wave velocity, etc., each attribute characterizing an aspect of the seismic wave data, and each attribute reflecting only certain geological information (e.g., lithology). In specific implementation, various types of seismic attributes and response characteristics of the underground river reservoir can be analyzed, and the seismic attribute with the most obvious response to the underground river reservoir is preferably selected.

Then, data filtering can be carried out on the optimized seismic attributes, the preliminary form of the underground river reservoir body is carved by combining a manual correction method, and finally the three-dimensional gridding is carried out to form an initial underground river training image. As shown in fig. 2(a) and 2 (b). Wherein, fig. 2(a) is a schematic diagram of preferred seismic attributes, which are most responsive to underground river reservoirs; FIG. 2(b) is a schematic diagram of an initial underground river training image obtained after a simple filtering process of seismic attributes, wherein X01-X13 represent the locations of wellbores.

And then, correcting the diameter of the underground river in the initial underground river training image by using outcrop diameter data of the underground river karst cave of the target work area. Although the initial underground river training image may represent the spatial three-dimensional form of the underground river, the underground river diameter represented in the image usually has a certain discrepancy with the real underground river reservoir.

In the embodiment of the description, the diameter of a karst cave section (i.e., outcrop) of the underground river in the target work area exposed in the surface rock stratum can be measured, the diameter of the underground river in the initial underground river training image is corrected according to the outcrop diameter of the karst cave, and the corrected training image is used as the underground river training image of the target work area and further used as the input data of the subsequent multipoint geostatistical simulation underground river.

The diameter of the outcrop of the underground river karst cave of the target work area can be measured to be 2-10 meters, and the diameter of the initial underground river training image can be 50-100 meters. The diameter of the outcrop of the underground river karst cave of the target work area is used for correcting the diameter of the underground river on the initial training image, and the accuracy of the training image can be greatly improved.

In another embodiment of the present specification, an initial underground river training image of the target work area may be constructed by using measured data of an underground river in a surface karst area similar to the target work area, and then, a diameter data of an underground river karst cave outcrop of the target work area is used to correct a diameter of the underground river in the initial underground river training image, so as to obtain the underground river training image of the target work area.

The method can acquire the field survey data of the underground river in the surface karst area similar to the target work area, and the data is subjected to three-dimensional gridding to form an initial underground river training image of the target work area. As shown in fig. 3(a) and 3 (b). In which fig. 3(a) is a plan spread diagram of a similar ground survey of the subsurface river of the surface karst region, north in fig. 3(a) indicates the north direction, and meter indicates meter. Fig. 3(b) is a schematic diagram of an initial underground river training image formed after the underground river is three-dimensionally gridded.

And then, correcting the obtained initial underground river training image by using outcrop diameter data of the underground river karst cave of the target work area, wherein the correction method is the same as the embodiment, and the description is omitted here.

In another embodiment of the present disclosure, the correcting the diameter of the underground river in the initial underground river training image by using outcrop diameter data of the underground river karst cave of the target work area may include:

First, the diameter of the initial underground river training diagram (fig. 4(a)) and the outcrop diameter of the underground river cavern of the target work area (fig. 4(b)) that have been constructed can be counted, and corresponding cumulative frequency curves can be respectively created, as shown in fig. 4(c) and 4 (d). Wherein, fig. 4(c) shows a schematic diagram of the cumulative frequency curve of the diameter of the underground river in the initial training image, and fig. 4(d) shows a schematic diagram of the cumulative frequency curve of the diameter of the underground river in the target work area determined according to outcrop diameter data of the karst cave of the underground river in the target work area.

Then, for the initial diameter of the target point on the initial underground river training graph, the cumulative frequency corresponding to the initial diameter is found on the cumulative frequency curve of the underground river diameter in the initial training image, the diameter corresponding to the same cumulative frequency is found on the cumulative frequency curve of the outcrop diameter of the underground river karst cave in the target work area as the corrected diameter, and then the diameter of the target point on the initial underground river training graph is replaced by the corrected diameter. According to the method, any point on the initial underground river training image is processed, and correction processing of the diameter of the underground river in the initial underground river training image is achieved.

Specifically, for a diameter of a certain point on the initial underground river training graph (for example, the diameter indicated by an arrow in fig. 4(a) is 82 meters), a cumulative frequency corresponding to the diameter is found on a cumulative frequency curve (for example, 55% for the diameter of 82 meters in fig. 4 (c)), and then a diameter corresponding to the same cumulative frequency is found on a cumulative frequency curve for the outcrop diameter (for example, 3.7 meters for the diameter of 55% for the cumulative frequency in fig. 4 (d)). Finally, the diameter of the point on the initial underground river training graph (indicated by the arrow in fig. 4(a)) is corrected to 3.7 meters.

According to the above method, the diameter at each point on the initial underground river training image is corrected to obtain the final training image, as shown in fig. 4(e), in which the diameter at the arrow is 3.7 m. Fig. 4(e) shows an underground river training image of a target work area for subsequent multi-point geostatistical simulation in the embodiment of the present specification.

According to outcrop diameter data of the underground river karst caves in the target work area, the underground river diameter in the initial underground river training image is corrected by combining the method of the accumulated frequency, so that the corrected overall diameter distribution of the underground river is more accurate, and meanwhile, the three-dimensional model is more in line with geological rules.

S4: and determining the development probability data of the underground river reservoir body according to the seismic attribute data and the karst ancient landform data of the target work area.

Seismic attribute data of the target work area can be obtained, and the underground river reservoir body development probability of the target work area is determined according to the seismic attribute data. And then, karst ancient landform data of the target work area can be obtained, and the underground river reservoir body development probability of the target work area is determined according to the karst ancient landform data. Finally, the two underground river reservoir development probabilities obtained above can be fused to form an underground river reservoir development integrated probability.

Generally, the karst area can be divided into three karst ancient landform facies zones on a plane according to the elevation of the karst area ancient landform: karst plateau, karst slope, and karst depression. And (3) counting the relationship of the development probability of the underground river reservoir bodies in different facies along with the vertical depth according to different karst ancient landform facies. The probability of the underground river reservoir body of different phase bands has certain difference, and the accuracy of determining the underground river reservoir body development probability can be improved by further considering the influence of the phase bands on the underground river reservoir body development probability.

In an embodiment of the present specification, the determining the underground river reservoir body development probability data may further include:

First, the seismic attribute that responds best to the underground river reservoir may be selected among the plurality of seismic attributes, and the preferred method may refer to the above-described embodiment. Then, the numerical relationship between the underground river development probability and the seismic attribute, such as the curve and the corresponding formula in fig. 5, can be counted according to the underground river reservoir body development section determined by the multi-well logging interpretation. Fig. 5 is a schematic diagram showing a change relationship between the development probability of the inland river and the seismic wave impedance attribute. And finally, expanding the statistical functional relation (namely the first functional relation) (such as a formula in fig. 5) to a well-free area, and calculating the underground river development probability of each point in space according to the seismic attributes, namely the underground river reservoir development conditional probability body constrained by the seismic attributes, so as to obtain the first underground river reservoir development probability data of the target work area.

And (3) counting the development probability of the underground river reservoir bodies corresponding to different depths in different facies zones according to the underground river reservoir body development sections explained by multi-well logging aiming at different karst ancient landform facies zones (karst plateau, karst slope and karst depression) to obtain a second functional relation, wherein the second functional relation is shown in figure 6. Fig. 6 is a schematic diagram of the development probability of underground river reservoir bodies corresponding to different depths in different facies zones. And assigning the second function relation to a karst ancient landform phase band corresponding to the target work area to be used as a karst ancient landform constraint underground river reservoir body development condition probability body, so as to obtain second underground river reservoir body development probability data of the target work area.

Then, the first underground river reservoir body development probability and the second underground river reservoir body development probability data at any point of the target work area can be fused, for example, the first underground river reservoir body development probability and the second underground river reservoir body development probability can be fused by adopting methods such as weighted average and the like, and the comprehensive development probability of the underground river reservoir body at the corresponding space point can be obtained. According to the method, all space points of the target work area are processed, and the underground river reservoir comprehensive development probability data body of the target work area is obtained.

In an embodiment of the present specification, the determining the comprehensive development probability data of the underground river reservoir may further include:

The seismic attribute constraint underground river reservoir body development condition probability body and the karst ancient landform constraint underground river reservoir body development condition probability body are not independent, and the underground river reservoir body development comprehensive probability body under the common constraint is calculated by directly adopting a method such as multiplying the two, so that the accuracy of the result is influenced. In the embodiment of the description, the first underground river reservoir body development probability data and the second underground river reservoir body development probability data are fused according to a non-independent conditional probability fusion method to obtain the comprehensive development probability data of the underground river reservoir body, so that the accuracy of data processing can be further improved.

In some embodiments, the two independent conditional probabilities may be fused, for example, by a pr (probability of rates) probability fusion method, to finally form a subsurface river reservoir development comprehensive probability under a multi-condition common constraint. The calculation model corresponding to the PR probability fusion method may be represented as:

in the formula, A represents that an underground river appears at a certain point, B, C represents seismic data and karst ancient landform data of the point respectively, and the two are not independent; p (A | B, C) represents the probability that the point will appear in the underground river under the control of seismic data and karst ancient landform data; p { A } represents the marginal probability of underground river development under the unconditional constraint, which is obtained by the statistics of well logging interpretation data; p { A | B }, P { A | C } respectively represent the probability of the point appearing in the underground river under the constraint of seismic data and karst ancient landform data.

In other embodiments, of course, the probability data of the underground river under the above two conditions may be fused by using a tau model or other similar non-independent condition fusion method to determine the comprehensive development probability data of the underground river reservoir.

S6: and under the constraint of the comprehensive development probability data of the underground river reservoir body, establishing an underground river reservoir body geological model of the target work area by using the logging interpretation data and the underground river training image as input data and utilizing a multi-point geostatistical simulation algorithm.

The multi-point geostatistical modeling algorithm may include a method of predicting unknown points based on known point classes. In the embodiment of the description, the underground river reservoir body development segment interpreted by logging can be used as known modeling data, and the development probability of the underground river reservoir body at the unknown point in space can be simulated and determined by using a multipoint simulation algorithm in combination with the underground river training image determined in the embodiment.

And then, further taking the comprehensive development probability data of the underground river reservoir body as constraint data, and determining the final underground river reservoir body development probability at the unknown point of the space so as to obtain the underground river reservoir body three-dimensional space distribution geological model of the target work area. In some embodiments, the development probability of the underground river reservoir obtained by multipoint simulation and the comprehensive development probability determined in the above embodiments may be fused by using a non-independent conditional fusion method such as PR and τ models, so as to obtain the final development probability of the underground river reservoir.

And then, determining the three-dimensional space distribution of the underground river reservoir body of the target work area according to the final development probability of the underground river reservoir body. In some embodiments, if the probability of belonging to a river reservoir at any point is greater than the probability of not belonging to a river reservoir, then that point may be determined to be a river reservoir. In other embodiments, a threshold value may be set, and a point where the probability of belonging to the underground river reservoir is greater than the threshold value is determined as the underground river reservoir location point. The threshold value may be set manually, and is not limited herein. In other embodiments, whether the point is the underground river or not can be randomly determined by taking the probability of belonging to the underground river reservoir at any point as a constraint. The random determination method may be a computer-based random algorithm, which is not described herein again.

The underground river reservoir body geological model established by the multi-point geostatistics simulation algorithm can enable the simulation result to be more consistent with the geological rule, and the continuity of the underground river reservoir body obtained through simulation is better.

In order to make the solution in the embodiment provided in the present specification clearer, the present specification also provides a specific example of an actual region to be measured to which the above-described solution is applied. The specific implementation takes an oil field in northwest China as an example, and the underground river reservoir body of the oil field develops.

Fig. 7 is a flow chart of determining the three-dimensional spatial distribution of the underground river reservoir in the area by using the above method provided by the embodiment of the present specification, and as shown in fig. 7, the specific steps are as follows:

1. and acquiring underground river training images.

1.1, constructing an initial underground river training image:

a: and constructing an initial underground river training image according to the seismic attributes.

(1) Filtering the optimized seismic attributes to obtain the general form of the underground river;

(2) and manually correcting the general form of the underground river obtained in the previous step, and carrying out three-dimensional gridding to form an initial underground river training image.

B: and constructing an initial underground river training image of the target work area according to the actually measured underground river data of the surface karst area similar to the target work area.

Survey data for subsurface rivers of similar karst regions are three-dimensionally gridded to form an initial subsurface river training image.

1.2, correcting the diameter of the underground river in the initial underground river training image to obtain an underground river training image.

(1) Respectively making an underground river karst cave diameter accumulated frequency curve observed at outcrop and a underground river diameter accumulated frequency curve in an initial underground river training image according to related statistical data;

(2) and finding corresponding accumulated frequency values on the accumulated frequency curve according to the underground river diameter of each point on the initial underground river training image, and then finding diameter values corresponding to the same accumulated frequency on the underground river karst cave diameter accumulated frequency curve observed at the outcrop, wherein the diameter is the diameter to be corrected of the corresponding point on the initial underground river training image.

(3) According to the method, the diameter of each point on the initial underground river training image is corrected to obtain the final underground river training image.

Correspondingly, fig. 8 shows a flow chart for constructing the underground river training image of the region, and fig. 9 shows the underground river training image of the region constructed according to the seismic attributes.

2. And constructing a multivariate constraint underground river reservoir body development comprehensive probability body based on a PR method.

(1) Constructing a seismic attribute constrained underground river reservoir body development condition probability body;

(2) constructing a karst ancient landform constrained underground river reservoir body development condition probability body;

(3) and (3) calculating the underground river reservoir body development comprehensive probability body jointly constrained by seismic attributes and karst ancient landforms by using a PR (path matching) method.

Correspondingly, fig. 10 shows a flow chart of constructing a multivariate constraint underground river reservoir body development comprehensive probability body based on a PR method; FIG. 11 shows a seismic attribute constrained subsurface river reservoir development conditional probability volume; FIG. 12 shows karst paleography constrained subsurface river reservoir body development conditional probability volumes; figure 13 shows the underground river reservoir development integrated probability body constrained by both.

3. And constructing a three-dimensional space distribution model of the underground river reservoir body.

The underground river reservoir body development section in the shaft drilling is used as modeling hard data, the comprehensive probability body constructed in the example is used as constraint data, the underground river training image in the example is combined, and the three-dimensional space distribution of the underground river reservoir body in the research area is simulated by adopting a multi-point geostatistics algorithm, as shown in fig. 14. Accordingly, FIG. 14 represents a three-dimensional spatial distribution geological model of the subsurface river reservoir of the study area.

As can be seen from fig. 14, by using the scheme provided by the above embodiment of the present disclosure, the underground river form and scale can be simulated accurately and effectively, and the simulation result has good continuity and highly matches the existing data and geological rules. Meanwhile, a plurality of simulation implementations can be formed, and then uncertainty analysis of underground river reservoir body development is carried out. And the established model has high precision, is beneficial to guiding the efficient development of the oil field, and has simple and efficient operation.

The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. For details, reference may be made to the description of the related embodiments of the related processing, and details are not repeated herein.

The foregoing description has been directed to specific embodiments of this disclosure. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.

One or more embodiments of the present disclosure provide a method for establishing a geological model of an underground river reservoir body, which may determine comprehensive development probability data of the underground river reservoir body according to seismic attribute data and karst paleotopographic data of a target work area. And then, simulating the three-dimensional space distribution of the underground river reservoir body in the target work area by using a multi-point geostatistics algorithm by taking the underground river reservoir body development section drilled in the shaft as modeling hard data and comprehensive development probability data as constraint data and combining an underground river training image. By utilizing the embodiments of the specification, the underground river form and scale can be accurately and effectively simulated, the simulation result has good continuity, and the existing data and geological rules are highly matched.

Based on the method for establishing the underground river reservoir body geological model, one or more embodiments of the specification further provide a device for establishing the underground river reservoir body geological model. The apparatus may include systems, software (applications), modules, components, servers, etc. that utilize the methods described in the embodiments of the present specification in conjunction with hardware implementations as necessary. Based on the same innovative conception, embodiments of the present specification provide an apparatus as described in the following embodiments. Since the implementation scheme of the apparatus for solving the problem is similar to that of the method, the specific implementation of the apparatus in the embodiment of the present specification may refer to the implementation of the foregoing method, and repeated details are not repeated. As used hereinafter, the term "unit" or "module" may be a combination of software and/or hardware that implements a predetermined function. Although the means described in the embodiments below are preferably implemented in software, an implementation in hardware, or a combination of software and hardware is also possible and contemplated. Specifically, fig. 15 is a schematic block diagram illustrating an embodiment of an apparatus for creating a geologic model of a subsurface river reservoir, according to fig. 15, the apparatus may include:

the data acquisition module 102 may be configured to acquire an underground river training image and well logging interpretation data of a target work area;

the development probability determination module 104 may be configured to determine comprehensive development probability data of the underground river reservoir body according to the seismic attribute data of the target work area and the karst ancient landform data;

the geological model building module 106 may be configured to build the geological model of the underground river reservoir in the target work area by using the logging interpretation data and the underground river training image as input data and using a multi-point geostatistical simulation algorithm under the constraint of the comprehensive development probability data of the underground river reservoir.

In another embodiment of the present specification, the developmental probability determination model 104 may include:

the first development probability determining unit can be used for obtaining a first functional relation between the development probability of the underground river reservoir body and the seismic attribute according to the logging interpretation data and the logging peripheral seismic attribute data in a statistical manner, and calculating and obtaining first underground river reservoir body development probability data of the target work area according to the first functional relation and the seismic attribute data of the target work area;

the second development probability determining unit can be used for determining a second functional relationship between the development probability and the vertical depth of the underground river reservoir body in different karst ancient landform facies zones according to the logging interpretation data, and determining second underground river reservoir body development probability data of the target work area according to the second functional relationship and the corresponding karst ancient landform facies zones of the target work area;

and the comprehensive development probability determining unit can be used for determining the comprehensive development probability data of the underground river reservoir body of the target work area according to the first underground river reservoir body development probability data and the second underground river reservoir body development probability data.

By using the scheme in the embodiment, the accuracy of determining the underground river reservoir body development probability can be improved.

It should be noted that the above-described apparatus may also include other embodiments according to the description of the method embodiment. The specific implementation manner may refer to the description of the related method embodiment, and is not described in detail herein.

According to the device for establishing the geological model of the underground river reservoir body, provided by one or more embodiments of the specification, the comprehensive development probability data of the underground river reservoir body can be determined according to the seismic attribute data and the karst ancient landform data of the target work area. And then, simulating the three-dimensional space distribution of the underground river reservoir body in the target work area by using a multi-point geostatistics algorithm by taking the underground river reservoir body development section drilled in the shaft as modeling hard data and comprehensive development probability data as constraint data and combining an underground river training image. By utilizing the embodiments of the specification, the underground river form and scale can be accurately and effectively simulated, the simulation result has good continuity, and the existing data and geological rules are highly matched.

The method or apparatus provided by the present specification and described in the foregoing embodiments may implement service logic through a computer program and record the service logic on a storage medium, where the storage medium may be read and executed by a computer, so as to implement the effect of the solution described in the embodiments of the present specification. Accordingly, the present specification also provides an apparatus for creating a subsurface river reservoir geological model, comprising a processor and a memory storing processor-executable instructions which, when executed by the processor, implement steps comprising:

The storage medium may include a physical device for storing information, and typically, the information is digitized and then stored using an electrical, magnetic, or optical media. The storage medium may include: devices that store information using electrical energy, such as various types of memory, e.g., RAM, ROM, etc.; devices that store information using magnetic energy, such as hard disks, floppy disks, tapes, core memories, bubble memories, and usb disks; devices that store information optically, such as CDs or DVDs. Of course, there are other ways of storing media that can be read, such as quantum memory, graphene memory, and so forth.

It should be noted that the above description of the apparatus according to the method embodiment may also include other embodiments. The specific implementation manner may refer to the description of the related method embodiment, and is not described in detail herein.

According to the device for establishing the geological model of the underground river reservoir body, the comprehensive development probability data of the underground river reservoir body can be determined according to the seismic attribute data and the karst ancient landform data of the target work area. And then, simulating the three-dimensional space distribution of the underground river reservoir body in the target work area by using a multi-point geostatistics algorithm by taking the underground river reservoir body development section drilled in the shaft as modeling hard data and comprehensive development probability data as constraint data and combining an underground river training image. By utilizing the embodiments of the specification, the underground river form and scale can be accurately and effectively simulated, the simulation result has good continuity, and the existing data and geological rules are highly matched.

The specification also provides a system for establishing the underground river reservoir body geological model, which can be an independent system for establishing the underground river reservoir body geological model and can also be applied to various oil reservoir analysis systems. The system may be a single server, or may include a server cluster, a system (including a distributed system), software (applications), an actual operating device, a logic gate device, a quantum computer, etc. using one or more of the methods or one or more of the example devices of the present specification, in combination with a terminal device implementing hardware as necessary. The system for creating a subsurface river reservoir geological model may comprise at least one processor and a memory storing computer-executable instructions which, when executed by the processor, implement the steps of the method described in any one or more of the embodiments above.

It should be noted that the above-mentioned system may also include other implementation manners according to the description of the method or apparatus embodiment, and specific implementation manners may refer to the description of the related method embodiment, which is not described in detail herein.

The system for establishing the geological model of the underground river reservoir body can determine the comprehensive development probability data of the underground river reservoir body according to the seismic attribute data and the karst ancient landform data of the target work area. And then, simulating the three-dimensional space distribution of the underground river reservoir body in the target work area by using a multi-point geostatistics algorithm by taking the underground river reservoir body development section drilled in the shaft as modeling hard data and comprehensive development probability data as constraint data and combining an underground river training image. By utilizing the embodiments of the specification, the underground river form and scale can be accurately and effectively simulated, the simulation result has good continuity, and the existing data and geological rules are highly matched.

It should be noted that, the above-mentioned apparatus or system in this specification may also include other implementation manners according to the description of the related method embodiment, and a specific implementation manner may refer to the description of the method embodiment, which is not described herein in detail. The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the hardware + program class, storage medium + program embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and for the relevant points, refer to the partial description of the method embodiment.

Although the embodiments of the present disclosure refer to operations and data descriptions of acquiring, defining, interacting, calculating, determining, etc. training images, logging data, multi-point simulation algorithms, etc., the embodiments of the present disclosure are not limited to necessarily conforming to standard data models/templates or to the descriptions of the embodiments of the present disclosure. Certain industry standards, or implementations modified slightly from those described using custom modes or examples, may also achieve the same, equivalent, or similar, or other, contemplated implementations of the above-described examples. The embodiments using these modified or transformed data acquisition, storage, judgment, processing, etc. may still fall within the scope of the alternative embodiments of the present description.

The systems, devices, modules or units illustrated in the above embodiments may be implemented by a computer chip or an entity, or by a product with certain functions. One typical implementation device is a computer. In particular, the computer may be, for example, a personal computer, a laptop computer, a vehicle-mounted human-computer interaction device, a cellular telephone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

For convenience of description, the above devices are described as being divided into various modules by functions, and are described separately. Of course, when implementing one or more of the present description, the functions of each module may be implemented in one or more software and/or hardware, or a module implementing the same function may be implemented by a combination of multiple sub-modules or sub-units, etc. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.

Those skilled in the art will also appreciate that, in addition to implementing the controller as pure computer readable program code, the same functionality can be implemented by logically programming method steps such that the controller is in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers and the like. Such a controller may therefore be considered as a hardware component, and the means included therein for performing the various functions may also be considered as a structure within the hardware component. Or even means for performing the functions may be regarded as being both a software module for performing the method and a structure within a hardware component.

The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.

It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method or apparatus that comprises the element.

As will be appreciated by one skilled in the art, one or more embodiments of the present description may be provided as a method, system, or computer program product. Accordingly, one or more embodiments of the present description may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, one or more embodiments of the present description may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.

One or more embodiments of the present description may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. One or more embodiments of the present specification can also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.

The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the system embodiment, since it is substantially similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment. In the description of the specification, reference to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the specification. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.

The above description is only an example of the present specification, and is not intended to limit the present specification. Various modifications and alterations to this description will become apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present specification should be included in the scope of the claims of the present specification.

Claims

1. A method of creating a geological model of an underground river reservoir, comprising:

the underground river training image and the well logging interpretation data of the target work area are obtained, and the method comprises the following steps: constructing an initial underground river training image of a target work area by using actually measured underground river data of a surface karst area similar to the target work area, or constructing the initial underground river training image of the target work area by using seismic attribute data of the target work area; acquiring outcrop diameter data of an underground river karst cave of a target work area, and calculating to obtain accumulated frequency data of the diameter of a first underground river according to the outcrop diameter data; calculating to obtain second underground river diameter accumulated frequency data according to the underground river diameter data in the initial underground river training image; correcting the underground river diameter in the initial underground river training image according to the first underground river diameter accumulated frequency data and the second underground river diameter accumulated frequency data to obtain an underground river training image of a target work area;

2. The method of creating a subsurface river reservoir geological model as recited in claim 1, wherein said determining subsurface river reservoir integrated development probability data comprises:

3. The method of creating a subsurface river reservoir geological model as recited in claim 2, wherein said determining subsurface river reservoir integrated development probability data comprises:

4. An apparatus for creating a geological model of a subsurface river reservoir, the apparatus comprising:

the data acquisition module is used for acquiring underground river training images and well logging interpretation data of a target work area, and comprises the following components: constructing an initial underground river training image of a target work area by using actually measured underground river data of a surface karst area similar to the target work area, or constructing the initial underground river training image of the target work area by using seismic attribute data of the target work area; acquiring outcrop diameter data of an underground river karst cave of a target work area, and calculating to obtain accumulated frequency data of the diameter of a first underground river according to the outcrop diameter data; calculating to obtain second underground river diameter accumulated frequency data according to the underground river diameter data in the initial underground river training image; correcting the underground river diameter in the initial underground river training image according to the first underground river diameter accumulated frequency data and the second underground river diameter accumulated frequency data to obtain an underground river training image of a target work area;

5. The apparatus for creating a geologic model of a subsurface river reservoir as defined in claim 4, wherein said developmental probability determination module comprises:

6. An apparatus for creating a subsurface river reservoir geological model comprising a processor and a memory for storing processor-executable instructions which, when executed by the processor, implement steps comprising:

7. A system for creating a geological model of a subsurface river reservoir comprising at least one processor and memory storing computer executable instructions which when executed by the processor implement the steps of the method of any one of claims 1 to 3.