CN115688958A

CN115688958A - Interlayer distribution prediction method and device and computer equipment

Info

Publication number: CN115688958A
Application number: CN202110869107.XA
Authority: CN
Inventors: 刘利勤; 王小剑; 康强强; 蔡光凤; 蔡建钦; 郭守相; 张利平; 赵金玲; 张尧
Original assignee: Petrochina Co Ltd
Current assignee: Petrochina Co Ltd
Priority date: 2021-07-30
Filing date: 2021-07-30
Publication date: 2023-02-03

Abstract

The application discloses a method and a device for predicting interlayer distribution and computer equipment, and belongs to the technical field of oil and gas development. The embodiment of the application provides a method for predicting interlayer distribution, in the method, interlayer distribution data of multiple types of interlayers, distribution training images of the multiple types of interlayers and distribution probability images of each type of interlayer are taken as constraint data to predict the spatial distribution of the multiple types of interlayers in a work area, and the prediction is constrained by the multiple types of data, so that the continuity of prediction results is good, and the prediction accuracy is improved.

Description

Interlayer distribution prediction method and device and computer equipment

Technical Field

The application relates to the technical field of oil and gas development. In particular to a method and a device for predicting interlayer distribution and computer equipment.

Background

In domestic oil and gas resources, the braided river reservoir body is taken as an important component part and has great contribution to oil and gas capacity. However, various types of interlayers, such as flooding type interlayers, waste river type interlayers, and silt type interlayers, develop in the braided river reservoirs. The interlayer shapes and scales of different types are different greatly, and the spatial distribution is complex, so that the prediction of the spatial distribution of the braided river reservoir interlayers is very important for developing oil and gas.

In the related technology, the distribution of braided river reservoir body interlayers is predicted mainly by a sequential indication simulation method, the method mainly comprises the steps of obtaining interlayer distribution data of interlayers of different types of interlayers, wherein the interlayer distribution data are the distribution data of interlayers of each drilled single well in the drilling process, using scale parameters and development directions of the interlayers of different types of interlayers in a variation function mode as constraint conditions in the prediction process, and then predicting the interlayer distribution of regions except the drilled single wells in a work area through a sequential indication simulation algorithm based on the interlayer distribution data and the constraint conditions.

However, when the method in the related art carries out prediction, the constrained data is less, the continuity of the prediction result is poor, and the accuracy of the prediction is low.

Disclosure of Invention

The embodiment of the application provides a method and a device for predicting interlayer distribution and computer equipment, which can improve the accuracy of interlayer space distribution prediction. The specific technical scheme is as follows:

in one aspect, an embodiment of the present application provides a method for predicting interlayer distribution, where the method includes:

acquiring surveying data of various types of interlayers, and acquiring interlayer information of the various types of interlayers based on the surveying data;

determining a first distribution training image based on interlayer information of the multiple types of interlayers, wherein the first distribution training image is used for predicting the distribution of the braided river reservoir interlayers;

acquiring seismic data of a work area where a target braided river reservoir body is located, and extracting attribute information of target seismic attributes from the seismic data;

acquiring interlayer distribution data of multiple types of interlayers, wherein the interlayer distribution data are the distribution data of interlayers in the drilling process of each single well in multiple single wells drilled in the work area;

determining a distribution probability image of each type of interlayer in the work area based on the attribute information of the target seismic attribute and the interlayer distribution data;

and predicting the spatial distribution of the multiple types of interlayers in the work area based on the first distribution training image, the interlayer distribution data and the distribution probability image.

In one possible implementation, the determining a first distribution training image based on the interlayer information of the multiple types of interlayers includes:

determining a distribution training subimage of each type of interlayer based on the interlayer information of each type of interlayer;

and integrating the distribution training subimages of each type of interlayer into one image to obtain the first distribution training image.

In another possible implementation manner, the integrating the distribution training subimages of each type of interlayer into one image to obtain the first distribution training image includes:

integrating multiple types of interlayers into one image based on the position of each type of interlayer in the distribution training sub-image of each type of interlayer;

and under the condition that multiple types of interlayers appear at the same position, based on the priority of each type of interlayer, taking the interlayer with high priority as the interlayer corresponding to the position to obtain the first distribution training image.

integrating the distribution training subimages of each type of interlayer into one image to obtain a second distribution training image;

and under the condition that the interlayer distribution of a position in the second distribution training image does not accord with the geological deposition development rule, correcting the interlayer of the position to obtain the first distribution training image.

In another possible implementation manner, the determining a distribution probability image of each type of interlayer in the work area based on the attribute information of the target seismic attribute and the interlayer distribution data includes:

determining a correlation relationship between the distribution probability of each type of interbed at different positions in a first area and the attribute information of the target seismic attribute based on the interbed distribution data, wherein the first area is the area where the drilled single wells are located;

taking the correlation relationship as a target correlation relationship, wherein the target correlation relationship is a relationship between the distribution probability of each type of interlayer at different positions in a second region and the attribute information of the target seismic attribute, and the second region is a region except the first region in the work area;

for any position in the second area, determining the distribution probability of each type of interlayer corresponding to the any position based on the target correlation relation and the attribute information of the target seismic attribute corresponding to the any position;

and integrating the distribution probability of each position and each type of interlayer corresponding to each position in the second region and the distribution probability of each position and each type of interlayer corresponding to each position in the first region into an image based on the size of the region of the work area to obtain the distribution probability image.

In another possible implementation manner, the predicting, based on the first distribution training image, the interlayer distribution data, and the distribution probability image, the spatial distribution of multiple types of interlayers within the work area includes:

marking multiple types of interlayers at different positions in a first area in a pre-established spatial distribution grid corresponding to the work area based on the positions of the drilled single wells and the interlayer distribution data, wherein the first area is the area where the drilled single wells are located;

randomly determining a current predicted position in the work area, and determining a first distribution probability of each type of interlayer corresponding to the current predicted position based on the first distribution training image, the positions of the drilled single wells and each previous predicted position, wherein the first distribution probability is an interlayer distribution probability constrained by geological deposition and development rules;

determining a second distribution probability of each type of interlayer corresponding to the current prediction position based on the distribution probability image, wherein the second distribution probability is an interlayer distribution probability constrained by the target seismic attribute;

determining a third distribution probability of each type of interlayer corresponding to the current prediction position based on the first distribution probability and the second distribution probability, wherein the third distribution probability is an interlayer distribution probability which is jointly constrained by the geological deposition and development rule and the target seismic attribute;

and predicting the spatial distribution of the multiple types of interlayers in the work area based on the third distribution probability, and displaying the spatial distribution of the multiple types of interlayers through the spatial distribution grid.

In another possible implementation, the determining, based on the first distribution training image, the locations of the drilled single wells, and each of the previous predicted locations, a first distribution probability for each type of interbed corresponding to the current predicted location includes:

determining a zonal distribution template based on the locations of the plurality of drilled individual wells, each of the previous predicted locations, and the current predicted location;

and scanning the first distribution training image through the interlayer distribution sample plate, and determining the first distribution probability of each type of interlayer corresponding to the current prediction position.

In another aspect, an embodiment of the present application provides an interlayer distribution prediction apparatus, where the apparatus includes:

the first acquisition module is used for acquiring survey data of various types of interlayers and acquiring interlayer information of various types of interlayers based on the survey data;

a first determination module, configured to determine a first distribution training image based on interlayer information of the multiple types of interlayers, where the first distribution training image is used to predict a distribution of braided river reservoir interlayers;

the extraction module is used for acquiring seismic data of a work area where a target braided river reservoir body is located and extracting attribute information of target seismic attributes from the seismic data;

the second acquisition module is used for acquiring interlayer distribution data of multiple types of interlayers, wherein the interlayer distribution data are the distribution data of interlayers in the drilling process of each of multiple single wells drilled in the work area;

the second determining module is used for determining a distribution probability image of each type of interlayer in the work area based on the attribute information of the target seismic attribute and the interlayer distribution data;

and the prediction module is used for predicting the spatial distribution of the multiple types of interlayers in the work area based on the first distribution training image, the interlayer distribution data and the distribution probability image.

In a possible implementation manner, the first determining module is configured to determine a distribution training subimage of each type of interlayer based on the interlayer information of each type of interlayer; and integrating the distribution training subimages of each type of interlayer into one image to obtain the first distribution training image.

In another possible implementation manner, the first determining module is configured to integrate multiple types of interlayers into one image based on the position of each type of interlayer in the distribution training sub-image of each type of interlayer; and under the condition that multiple types of interlayers appear at the same position, taking the interlayer with high priority as the interlayer corresponding to the position based on the priority of each type of interlayer to obtain the first distribution training image.

In another possible implementation manner, the first determining module is configured to integrate the distribution training subimages of each type of interlayer into one image to obtain a second distribution training image; and under the condition that the interlayer distribution of a position in the second distribution training image does not accord with the geological deposition development rule, correcting the interlayer of the position to obtain the first distribution training image.

In another possible implementation manner, the second determining module is configured to determine, based on the interbed distribution data, a correlation between a distribution probability of each type of interbed at different positions in a first area and the attribute information of the target seismic attribute, where the first area is located by the drilled single wells; taking the correlation relationship as a target correlation relationship, wherein the target correlation relationship is a relationship between the distribution probability of each type of interlayer at different positions in a second region and the attribute information of the target seismic attribute, and the second region is a region except the first region in the work area; for any position in the second area, determining the distribution probability of each type of interlayer corresponding to the any position based on the target correlation relation and the attribute information of the target seismic attribute corresponding to the any position; and integrating the distribution probability of each position and each type of interlayer corresponding to each position in the second region and the distribution probability of each position and each type of interlayer corresponding to each position in the first region into an image based on the size of the region of the work area to obtain the distribution probability image.

In another possible implementation manner, the prediction module is configured to label, based on the positions of the drilled single wells and the interlayer distribution data, multiple types of interlayers at different positions in a first area in a pre-established spatial distribution grid corresponding to the work area, where the first area is a region where the drilled single wells are located; randomly determining a current prediction position in the work area, and determining a first distribution probability of each type of interlayer corresponding to the current prediction position based on the first distribution training image, the positions of the drilled single wells and each previous prediction position, wherein the first distribution probability is an interlayer distribution probability constrained by a geological deposition and development law; determining a second distribution probability of each type of interlayer corresponding to the current prediction position based on the distribution probability image, wherein the second distribution probability is an interlayer distribution probability constrained by the target seismic attribute; determining a third distribution probability of each type of interlayer corresponding to the current prediction position based on the first distribution probability and the second distribution probability, wherein the third distribution probability is an interlayer distribution probability constrained by the geological deposition and development rule and the target seismic attribute together; and predicting the spatial distribution of the multiple types of interlayers in the work area based on the third distribution probability, and displaying the spatial distribution of the multiple types of interlayers through the spatial distribution grids.

In another possible implementation, the prediction module is configured to determine a zonal distribution template based on the locations of the drilled single wells, each of the previous predicted locations, and the current predicted location; and scanning the first distribution training image through the interlayer distribution sample plate, and determining the first distribution probability of each type of interlayer corresponding to the current prediction position.

In another aspect, an embodiment of the present application provides a computer device, where the computer device includes a processor and a memory, where the memory stores at least one program code, and the at least one program code is loaded and executed by the processor to implement the operations performed in the interlayer distribution prediction method in the embodiment of the present application.

In another aspect, an embodiment of the present application provides a computer-readable storage medium, where at least one program code is stored, and the at least one program code is loaded into and executed by a processor, so as to implement the operations performed in the interlayer distribution prediction method in the embodiment of the present application.

In another aspect, the present application provides a computer program product or a computer program, where the computer program product or the computer program includes computer program code, and the computer program code is stored in a computer readable storage medium. The processor of the computer device reads the computer program code from the computer-readable storage medium, and the processor executes the computer program code to implement the operations performed in the interlayer distribution prediction method in the embodiments of the present application.

The technical scheme provided by the embodiment of the application has the following beneficial effects:

the embodiment of the application provides a method for predicting interlayer distribution, in the method, interlayer distribution data of multiple types of interlayers, distribution training images of the multiple types of interlayers and distribution probability images of each type of interlayer are used as constraint data to predict the spatial distribution of the multiple types of interlayers in a work area, and due to the fact that the spatial distribution is constrained by the multiple types of data when prediction is carried out, the continuity of prediction results is good, and therefore the accuracy of prediction is improved.

Drawings

Fig. 1 is a flowchart of a method for predicting interlayer distribution according to an embodiment of the present application;

FIG. 2 is a schematic diagram of a field outcrop survey according to an embodiment of the present disclosure;

FIG. 3 is a diagram illustrating a width distribution of a landing interlayer according to an embodiment of the present disclosure;

fig. 4 is a schematic view of a planar spread of a braided river reservoir interbed provided by an embodiment of the present application;

FIG. 5 is a schematic diagram of the correlation between the width and thickness of an interlayer provided in the embodiments of the present application;

FIG. 6 is a schematic diagram of a distributed training subimage of a flooding sandwich provided by an embodiment of the present application;

FIG. 7 is a schematic diagram of a distribution training subimage of a waste riverway type interlayer provided by an embodiment of the present application;

FIG. 8 is a schematic diagram of a distribution training subimage of a fallout interlayer provided in an embodiment of the present application;

FIG. 9 is a schematic illustration of a layered deposit development law for a braided river reservoir provided in an embodiment of the present application;

FIG. 10 is a graphical illustration of the correlation between distribution probability and root mean square amplitude for a flooding sandwich provided by an embodiment of the present application;

FIG. 11 is a schematic diagram illustrating interlayer distribution prediction based on a multi-point geostatistical algorithm according to an embodiment of the present application;

FIG. 12 is a schematic diagram of a distributed training image provided by an embodiment of the present application;

FIG. 13 is a schematic illustration of a distribution probability image for a flooded sandwich provided in an embodiment of the present application;

FIG. 14 is a schematic illustration of a braided river reservoir interbed spatial distribution provided by an embodiment of the present application;

fig. 15 is a schematic structural diagram of a device for predicting interlayer distribution according to an embodiment of the present application;

fig. 16 is a block diagram of a computer device according to an embodiment of the present disclosure.

Detailed Description

In order to make the technical solutions and advantages of the present application more clear, the following describes the embodiments of the present application in further detail.

The following description will first explain the words used in the examples of the present application:

braided river: one type of river is developed in mountainous areas or upstream river sections of rivers, and is formed by multiple river channels which are branched and converged for multiple times to form a braid shape, so that the river channel is wide and shallow, has small curvature, is not fixed and migrates rapidly, and is also called as a swimming river.

Braided river reservoir: the braided river developed in the geological history period is mainly used for depositing medium and coarse sandstones, fine sandstones and a small amount of flooding plain mudstones and siltstones, then is buried by later-stage deposition and is influenced by various geological actions, part of inter-particle pores are filled with mud, rock debris and the like, part of the pores are reserved to form a storage space, and the braided river has certain storage capacity.

Braided river reservoir body interlayer: the muddy sediment developed in the braided river reservoir body is formed by longitudinal multi-stage superposition and transverse back-and-forth swing of the braided river and different-stage river channel flushing and filling superposition, and simultaneously, a sediment layer and other muddy sediment are developed in a single-stage river channel, and the impermeability of the muddy sediment has very important influence on the distribution of residual oil. The braided river reservoir body interlayer can be mainly divided into a flooding type interlayer, a waste river channel type interlayer and a silt falling type interlayer.

Exposing heads: the same layer of rock can extend from underground to the surface of the rock section developing on the surface of the earth, so that the underground condition of the rock layer can be known by observing outcrop. In the embodiment of the application, part of the interlayer of the same layer is exposed to the surface, and the interlayer in the underground part controls the distribution of the residual oil gas.

The embodiment of the present application provides a method for predicting interlayer distribution, which is executed by a computer device, and with reference to fig. 1, the method includes:

step 101: the computer device obtains survey data for the multiple types of interlayers, and obtains interlayer information for the multiple types of interlayers based on the survey data.

In the embodiment of the application, the braided river reservoir mainly comprises three types of interlayers which are respectively a waste river channel type interlayer, a flooding type interlayer and a silt type interlayer. The interlayer information may include at least one of a geometry of each type of interlayer, a direction of development of each type of interlayer, a plurality of scale parameters for each type of interlayer, and a correlation between the plurality of scale parameters, which may include at least one of a length, a width, and a thickness of the interlayer for each type of interlayer. The correlation may be a correlation between the thickness and the width, or a correlation between the length and the width, which is not particularly limited in the embodiments of the present application.

The computer equipment can firstly acquire survey data for surveying various types of interlayers of wild outcrops and other work area braided river reservoirs on the spot, and acquires the geometric forms of various types of interlayers, a plurality of scale parameters and the correlation among the scale parameters according to the survey data.

Referring to fig. 2, a computer device may obtain a profile of a wild outcrop braided river reservoir interbed from survey data for multiple types of interbed in the wild outcrop, and determine a geometry of the slough-type interbed from the profile, which may be elliptical, circular, or any other shape. Referring to fig. 3, the computer device may further determine the width of the silt-fall interlayer according to the profile of the silt-fall interlayer in fig. 2, and determine the width distribution diagram of the silt-fall interlayer according to the proportion of different widths.

Referring to fig. 4, the computer device may obtain a planar spread of braided river reservoir interlayers from survey data of other regions of the braided river reservoir, and determine a development direction of each type of interlayer from the planar spread. Referring to fig. 5, the computer device may determine the width and thickness of each type of interbed and the correlation between the width and thickness from survey data for other zonal braided river reservoirs.

Step 102: the computer device determines a distribution training sub-image for each type of band based on band information for each type of band.

The computer equipment can establish a three-dimensional spatial distribution grid firstly, and for each type of interlayer, the computer equipment carries out three-dimensional gridding processing on the type of interlayer on the basis of determining interlayer information such as geometric forms, scale parameters and the like of the type of interlayer to obtain a distribution training subimage of the type of interlayer.

Referring to fig. 6, 7 and 8, respectively, fig. 6 is a distribution training sub-image of a flooding-type interlayer, fig. 7 is a distribution training sub-image of a waste river-type interlayer, and fig. 8 is a distribution training sub-image of a falling-silt type interlayer. In fig. 6, 7, and 8, the numbers X01, X02, and the like each indicate the well numbers of a plurality of single wells that have been drilled.

Step 103: the computer device integrates the distributed training subimages of each type of interlayer into one image to obtain a first distributed training image.

The distribution training subimages obtained in step 102 are independently constructed by respective scale parameters and geometric forms, and the combination relationship is not consistent with the geological deposition development law, so that each type of interlayer needs to be combined to obtain a final distribution training image by taking the geological deposition development law as guidance.

In step 103, the computer device may integrate multiple types of interlayers into one image based on the position of each type of interlayer in the distribution training sub-image of each type of interlayer, and in the case that multiple types of interlayers occur at the same position, based on the priority of each type of interlayer, take the interlayer with high priority as the interlayer corresponding to the position, to obtain a first distribution training image. The computer device can take sandstone as a background phase and integrate multiple types of interlayers at different positions into one image.

Before the step, the computer equipment can determine the priority sequence of the multiple types of interlayers by taking the geological mode as a constraint condition and taking the geological cause as a standard, wherein the priority sequence comprises a waste river channel type interlayer, a flooding type interlayer and a silt-falling type interlayer from high to low. Namely, when multiple types of interlayers appear in the same grid, the interlayer with low priority is covered by the interlayer with high priority, and therefore the first distribution training image is obtained.

It should be noted that, in the case where the computer device integrates the image into which the interlayer of the position possibly existing does not conform to the geological deposit development law, the image needs to be corrected. Accordingly, the process may be: the computer equipment integrates the distribution training subimages of each type of interlayer into one image to obtain a second distribution training image; and under the condition that the interlayer distribution at a position in the second distribution training image does not accord with the geological deposition development rule, correcting the interlayer at the position to obtain a first distribution training image. Wherein, the geological deposition development rule is the development rule of the braided river reservoir interlayer type, see fig. 9.

Step 104: and the computer equipment acquires the seismic data of the work area where the target braided river reservoir body is located, and extracts the attribute information of the target seismic attribute from the seismic data.

The target seismic attributes may be seismic attributes such as root-mean-square amplitude, average amplitude, and the like, and in the embodiment of the present application, the target seismic attributes are not specifically limited. The attribute information of the target seismic attribute is an attribute value of the target seismic attribute. For example, if the target seismic attribute is the root-mean-square amplitude, the attribute information of the target seismic attribute is the magnitude of the root-mean-square amplitude.

The computer device may acquire seismic data acquired by exciting seismic waves through a plurality of shots, and extract attribute information of a target seismic attribute from the seismic data. The different types of interlayers have different responses to different seismic attributes, and the process of extracting the attribute information of the target seismic attribute from the seismic data by the computer equipment can be as follows: the computer equipment extracts a plurality of seismic attributes from the seismic data, determines the seismic attribute with the maximum response of the multi-type interlayer to the plurality of seismic attributes, and takes the seismic attribute as a target seismic attribute.

In a possible implementation mode, due to the fact that the resolution of seismic data is low, the seismic data does not have obvious response to the silt-falling interlayer, the silt-falling interlayer is difficult to be restrained by the seismic data, and the plane distribution of the seismic data can be restrained well by the waste river channel type interlayer and the flooding type interlayer. Thus, in embodiments of the present application, only the planar distribution of the abandoned river-type mezzanines and flooded mezzanines may be constrained by seismic data.

Step 105: and acquiring interlayer distribution data of various interlayers by the computer equipment.

The interlayer distribution data is the distribution data of the interlayer in the drilling process of each of a plurality of single wells drilled in the work area. For example, for any single well, if an overflowing interlayer is drilled at a certain position in the drilling process and drilling is continued, and a silt-falling interlayer is drilled at a deeper position, the computer equipment acquires the distribution condition of various types of interlayers in the drilling process to obtain interlayer distribution data.

Step 106: and the computer equipment determines a distribution probability image of each type of interlayer in the work area based on the attribute information of the target seismic attribute and the interlayer distribution data.

This step can be realized by the following steps (1) to (4), including:

(1) The computer device determines a correlation between the distribution probability of each type of interlayer at different positions within the first area and the attribute information of the target seismic attribute based on the interlayer distribution data.

The first zone is a zone in which a plurality of single wells are drilled.

For example, for a flooding-type interbed, which responds with the best seismic attribute as the root-mean-square amplitude, the computer device counts the numerical relationship, i.e., the correlation, between the distribution probability of the flooding-type interbed and the root-mean-square amplitude based on interbed distribution data interpreted by the plurality of single wells. Referring to fig. 10, it can be seen from fig. 10 that: for flooded interlayers, different magnitudes of root mean square amplitude correspond to different distribution probabilities.

(2) The computer device takes the correlation as a target correlation.

The target correlation relationship is a relationship between the distribution probability of each type of interlayer at different positions in a second region and the attribute information of the target seismic attribute, and the second region is a region except the first region in the work area.

(3) For any position in the second area, the computer equipment determines the distribution probability of each type of interlayer corresponding to the any position based on the target correlation relation and the attribute information of the target seismic attribute corresponding to the any position.

The target correlation relationship is a relationship between different positions in the second area, the distribution probability of each type of interlayer and the attribute information of the target seismic attribute, so that the distribution probability of each type of interlayer corresponding to any position can be determined according to the target correlation relationship under the condition that the position and the attribute information of the target seismic attribute corresponding to the position are determined. Correspondingly, the computer equipment expands the correlation to the well-free area to serve as the target correlation of the well-free area, and the distribution probability of each type of interlayer corresponding to each position on a plane is determined according to the attribute information of the target seismic attribute.

(4) And the computer equipment integrates the distribution probability of each type of interlayer corresponding to each position in the second area and the distribution probability of each type of interlayer corresponding to each position in the first area into one image based on the area size of the work area to obtain a distribution probability image.

For each type of interlayer, the computer equipment integrates the distribution probability of each position in the no-well region and the corresponding type of interlayer and the distribution probability of each position in the well region and the corresponding type of interlayer into one image to obtain a distribution probability image of the type of interlayer.

Step 107: the computer device predicts spatial distribution of multiple types of interlayers within the work area based on the first distribution training image, the interlayer distribution data, and the distribution probability image.

The multi-point geostatistics algorithm is an advanced method for establishing a complex geologic body model, can well condition data and can reproduce the geometric form of a target geologic body. The computer device predicts the spatial distribution of multiple types of interlayers in the work area by combining the first distribution training image and by using the interlayer distribution data as prediction hard data and the distribution probability image of each type of interlayer as constraint data through a multipoint geostatistics algorithm, as shown in fig. 11. Accordingly, the process can be realized by the following steps (1) to (5), including:

(1) And marking the multiple types of interlayers at different positions in the first area in a pre-established spatial distribution grid corresponding to the work area by the computer equipment based on the positions and interlayer distribution data of the drilled single wells.

The first area is the area where a plurality of drilled single wells are located, and the spatial distribution grid is a three-dimensional coordinate system and exactly corresponds to the area size of the work area. The computer device can label multiple types of interbeds at different locations in the spatial distribution grid based on the location and interbed distribution data for the plurality of individual wells that have been drilled.

(2) The computer equipment randomly determines a current prediction position in the work area, and determines a first distribution probability of each type of interlayer corresponding to the current prediction position based on the first distribution training image, the positions of the drilled single wells and each previous prediction position.

The first distribution probability is an interlayer distribution probability constrained by a geological deposition and development rule.

This step can be realized by the following steps (2-1) to (2-2), including:

(2-1) the computer device determines a zonal distribution template based on the locations of the plurality of single wells that have been drilled, each of the previous predicted locations, and the current predicted location.

If the current predicted position is the position where the prediction is performed for the first time, the computer device may connect the positions of the drilled single wells and the current predicted position, and use the obtained pattern as a sandwich distribution template.

If the current predicted position is not the position predicted for the first time, the computer device connects the positions of the drilled single wells, each previous predicted position and the current predicted position, and the obtained graph is used as a sandwich distribution sample plate. For example, if the current predicted position is the position predicted for the second time, the computer device connects the positions of the drilled single wells, the position predicted for the first time, and the current predicted position, and uses the obtained pattern as a sandwich distribution template. If the current prediction position is the position for predicting for the third time, the computer device connects the positions of the drilled single wells, the position for predicting for the first time, the position for predicting for the second time and the current prediction position, and uses the obtained graph as a sandwich distribution template.

And (2-2) the computer equipment scans the first distribution training image through the interlayer distribution sample plate and determines the first distribution probability of each type of interlayer corresponding to the current prediction position.

And each time of prediction is carried out, the computer equipment scans the first distribution training image once through the interlayer distribution sample plate to obtain the first distribution probability of each type of interlayer corresponding to the current time.

(3) And the computer equipment determines a second distribution probability of each type of interlayer corresponding to the current prediction position based on the distribution probability image of each type of interlayer.

The second distribution probability is the interbed distribution probability of the target seismic attribute constraint. In this step, the computer device may first determine an image position of the current predicted position in the distribution probability image of each type of interlayer, and then determine a second distribution probability of each type of interlayer corresponding to the image position.

(4) The computer device determines a third distribution probability of each type of interlayer corresponding to the current predicted position based on the first distribution probability and the second distribution probability.

The third distribution probability is an interlayer distribution probability constrained by the geological deposition development rule and the target seismic attribute together. The computer device can perform weighted summation on the first distribution probability and the second distribution probability to obtain a third distribution probability, and can also perform operation on the first distribution probability and the second distribution probability through other functional relations to obtain the third distribution probability. In the embodiments of the present application, this is not particularly limited.

(5) And the computer predicts the spatial distribution of the multiple types of interlayers in the work area based on the third distribution probability, and displays the spatial distribution of the multiple types of interlayers through the spatial distribution grid.

And the computer equipment determines the interlayer type with the maximum third distribution probability based on the third distribution probability of each type of interlayer corresponding to each predicted position. And the computer equipment determines whether the maximum third distribution probability exceeds a preset distribution probability, if so, the interlayer with the maximum third distribution probability is marked at the predicted position in the spatial distribution grid based on interlayer information such as geometric forms and scale parameters corresponding to the interlayer type, and if not, the predicted position is not the interlayer, possibly a sandstone background phase and does not need marking, so that the spatial distribution of multiple types of interlayers in the work area is finally obtained and displayed through the spatial distribution grid.

The embodiment of the application provides a method for predicting interlayer distribution, in the method, interlayer distribution data of multiple types of interlayers, distribution training images of the multiple types of interlayers and distribution probability images of each type of interlayer are taken as constraint data to predict the spatial distribution of the multiple types of interlayers in a work area, and the prediction is constrained by the multiple types of data, so that the continuity of prediction results is good, and the prediction accuracy is improved.

In addition, the method provided by the embodiment of the application can effectively simulate the geometric form and scale of each type of interlayer, is highly consistent with the existing data and geological deposition development rules, is consistent with the actual form, can form a plurality of simulation implementations, further performs uncertainty analysis on the distribution of the braided river reservoir interlayer, and is simple and efficient to operate. In addition, the prediction accuracy is high, and the method is favorable for guiding the efficient development of the oil field.

Taking an oil field in the eastern part of China as an example, the main reservoir of the oil field is a braided river reservoir which mainly develops a waste river interlayer, a flooding interlayer and a silt-falling interlayer, and the method provided by the embodiment of the application is adopted for the oil field to predict the spatial distribution of various interlayers.

First, a braided river reservoir interbed distribution training image is constructed, which can be seen in fig. 12.

Second, the distribution probability images of the waste-river-type interlayer and the flooding-type interlayer are determined, wherein the distribution probability image of the flooding-type interlayer can be seen in fig. 13.

And finally, forecasting the three-dimensional spatial distribution of various interlayers of the braided river reservoir in the work area by taking interlayer distribution data as spatial distribution forecasting hard data, taking distribution probability images of the waste river type interlayer and the flooding type interlayer as constraint data and combining distribution training images and adopting a multipoint geostatistics algorithm, and referring to fig. 14.

In the embodiment of the application, the process of predicting the spatial distribution of various interlayers is the process of establishing a model, the model is constrained by various data, such as interlayer distribution data, field outcrop survey data, geological deposition and development rules and the like, and the simulation result has good continuity and conforms to the actual form.

An embodiment of the present application provides an interlayer distribution prediction apparatus, which is characterized in that, referring to fig. 15, the apparatus includes:

the first acquisition module 1501 is configured to acquire survey data of multiple types of interlayers, and acquire interlayer information of the multiple types of interlayers based on the survey data;

a first determining module 1502, configured to determine a first distribution training image based on interlayer information of multiple types of interlayers, where the first distribution training image is used to predict distribution of interlayers of a braided river reservoir;

the extraction module 1503 is used for acquiring seismic data of a work area where a target braided river reservoir body is located and extracting attribute information of target seismic attributes from the seismic data;

a second obtaining module 1504, configured to obtain interlayer distribution data of multiple types of interlayers, where the interlayer distribution data is distribution data of an interlayer during drilling of each of multiple single wells drilled in a work area;

a second determining module 1505, configured to determine a distribution probability image of each type of interlayer in the work area based on the attribute information of the target seismic attribute and the interlayer distribution data;

and the prediction module 1506 is used for predicting the spatial distribution of the interlayers of various types in the work area based on the first distribution training image, the interlayer distribution data and the distribution probability image.

In a possible implementation manner, the first determining module 1502 is configured to determine a distribution training sub-image of each type of interlayer based on the interlayer information of each type of interlayer; and integrating the distribution training subimages of each type of interlayer into one image to obtain a first distribution training image.

In another possible implementation, the first determining module 1502 is configured to train a position of each type of interlayer in the sub-image based on a distribution of each type of interlayer, and integrate the plurality of types of interlayers into one image; under the condition that multiple types of interlayers appear at the same position, based on the priority of each type of interlayer, the interlayer with high priority is used as the interlayer corresponding to the position, and a first distribution training image is obtained.

In another possible implementation manner, the first determining module 1502 is configured to integrate the distribution training subimages of each type of interlayer into one image to obtain a second distribution training image; and under the condition that the interlayer distribution of a position in the second distribution training image does not accord with the geological deposition development rule, correcting the interlayer of the position to obtain a first distribution training image.

In another possible implementation, the second determining module 1505 is configured to determine a correlation between the distribution probability of each type of interbed at different positions in the first area and the attribute information of the target seismic attribute based on the interbed distribution data, where the first area is the area where the plurality of single wells are drilled; taking the correlation as a target correlation, wherein the target correlation is the relationship between the distribution probability of each type of interlayer at different positions in a second area and the attribute information of the target seismic attribute, and the second area is an area except the first area in the work area; for any position in the second area, determining the distribution probability of each type of interlayer corresponding to any position based on the target correlation relation and the attribute information of the target seismic attribute corresponding to any position; and integrating the distribution probability of each position and each type of interlayer corresponding to the position in the second region and the distribution probability of each position and each type of interlayer corresponding to the position in the first region into one image based on the size of the region of the work area to obtain a distribution probability image.

In another possible implementation manner, the prediction module 1506 is configured to label, based on the positions and interlayer distribution data of the drilled single wells, multiple types of interlayers at different positions in a first area in a pre-established spatial distribution grid corresponding to a work area, where the first area is an area where the drilled single wells are located; randomly determining a current prediction position in a work area, and determining a first distribution probability of each type of interlayer corresponding to the current prediction position based on the first distribution training image, the positions of the drilled single wells and each previous prediction position, wherein the first distribution probability is an interlayer distribution probability constrained by a geological deposition and development rule; determining a second distribution probability of each type of interlayer corresponding to the current prediction position based on the distribution probability image, wherein the second distribution probability is an interlayer distribution probability constrained by the target seismic attribute; determining a third distribution probability of each type of interlayer corresponding to the current prediction position based on the first distribution probability and the second distribution probability, wherein the third distribution probability is an interlayer distribution probability constrained by a geological deposition development rule and a target seismic attribute together; and predicting the spatial distribution of the multiple types of interlayers in the work area based on the third distribution probability, and displaying the spatial distribution of the multiple types of interlayers through a spatial distribution grid.

In another possible implementation, the prediction module 1506 is configured to determine a zonal distribution template based on the locations of the drilled single wells, each of the previous predicted locations, and the current predicted location; and scanning the first distribution training image through the interlayer distribution sample plate, and determining the first distribution probability of each type of interlayer corresponding to the current prediction position.

The embodiment of the application provides a device for predicting interlayer distribution, the device predicts the spatial distribution of multiple types of interlayers in a work area by taking interlayer distribution data of the multiple types of interlayers, distribution training images of the multiple types of interlayers and distribution probability images of each type of interlayer as constraint data, and the prediction is constrained by the multiple types of data, so that the continuity of a prediction result is good, and the prediction accuracy is improved.

Fig. 16 shows a block diagram of a computer device 1600 provided in an example embodiment of the present application. The computer device 1600 may be a portable mobile computer device such as: a smart phone, a tablet computer, an MP3 player (Moving Picture Experts Group Audio Layer III, motion video Experts compression standard Audio Layer 3), an MP4 player (Moving Picture Experts Group Audio Layer IV, motion video Experts compression standard Audio Layer 4), a notebook computer, or a desktop computer. Computer device 1600 may also be referred to by other names such as user device, portable computer device, laptop computer device, desktop computer device, etc.

Generally, computer device 1600 includes: a processor 1601, and a memory 1602.

The processor 1601 may include one or more processing cores, such as a 4-core processor, an 8-core processor, and so on. The processor 1601 may be implemented in at least one hardware form of a DSP (Digital Signal Processing), an FPGA (Field-Programmable Gate Array), and a PLA (Programmable Logic Array). Processor 1601 may also include a main processor and a coprocessor, where the main processor is a processor for Processing data in an awake state, and is also referred to as a Central Processing Unit (CPU); a coprocessor is a low power processor for processing data in a standby state. In some embodiments, the processor 1601 may be integrated with a GPU (Graphics Processing Unit), which is responsible for rendering and drawing content that the display screen needs to display. In some embodiments, the processor 1601 may further include an AI (Artificial Intelligence) processor for processing computing operations related to machine learning.

Memory 1602 may include one or more computer-readable storage media, which may be non-transitory. The memory 1602 may also include high-speed random access memory, as well as non-volatile memory, such as one or more magnetic disk storage devices, flash memory storage devices. In some embodiments, a non-transitory computer readable storage medium in memory 1602 is configured to store at least one instruction for execution by processor 1601 to implement the method for predicting interlayer distribution provided by the method embodiments herein.

In some embodiments, computer device 1600 may also optionally include: peripheral interface 1603 and at least one peripheral. The processor 1601, the memory 1602 and the peripheral interface 1603 may be connected via buses or signal lines. Various peripheral devices may be connected to peripheral interface 1603 via buses, signal lines, or circuit boards. Specifically, the peripheral device includes: at least one of a radio frequency circuit 1604, a display 1605, a camera assembly 1606, audio circuitry 1607, a positioning assembly 1608, and a power supply 1609.

Peripheral interface 1603 can be used to connect at least one peripheral associated with an I/O (Input/Output) to processor 1601 and memory 1602. In some embodiments, processor 1601, memory 1602, and peripheral interface 1603 are integrated on the same chip or circuit board; in some other embodiments, any one or two of the processor 1601, the memory 1602, and the peripheral interface 1603 may be implemented on separate chips or circuit boards, which is not limited by this embodiment.

The Radio Frequency circuit 1604 is used for receiving and transmitting RF (Radio Frequency) signals, also called electromagnetic signals. The radio frequency circuitry 1604 communicates with communication networks and other communication devices via electromagnetic signals. The rf circuit 1604 converts the electrical signal into an electromagnetic signal to be transmitted, or converts a received electromagnetic signal into an electrical signal. Optionally, the radio frequency circuit 1604 comprises: an antenna system, an RF transceiver, one or more amplifiers, a tuner, an oscillator, a digital signal processor, a codec chipset, a subscriber identity module card, and so forth. The radio frequency circuitry 1604 may communicate with other computer devices via at least one wireless communication protocol. The wireless communication protocols include, but are not limited to: the world wide web, metropolitan area networks, intranets, generations of mobile communication networks (2G, 3G, 4G, and 5G), wireless local area networks, and/or WiFi (Wireless Fidelity) networks. In some embodiments, the rf circuit 1604 may also include NFC (Near Field Communication) related circuits, which are not limited in this application.

The display 1605 is used to display a UI (User Interface). The UI may include graphics, text, icons, video, and any combination thereof. When the display screen 1605 is a touch display screen, the display screen 1605 also has the ability to capture touch signals on or over the surface of the display screen 1605. The touch signal may be input to the processor 1601 as a control signal for processing. At this point, the display 1605 may also be used to provide virtual buttons and/or a virtual keyboard, also referred to as soft buttons and/or a soft keyboard. In some embodiments, the display 1605 may be one, disposed on the front panel of the computer device 1600; in other embodiments, the display screens 1605 can be at least two, each disposed on a different surface of the computer device 1600 or in a folded design; in other embodiments, the display 1605 may be a flexible display disposed on a curved surface or on a folded surface of the computer device 1600. Even the display 1605 may be arranged in a non-rectangular irregular pattern, i.e., a shaped screen. The Display 1605 may be made of LCD (Liquid Crystal Display), OLED (Organic Light-Emitting Diode), or other materials.

The camera assembly 1606 is used to capture images or video. Optionally, camera assembly 1606 includes a front camera and a rear camera. Generally, a front camera is disposed on a front panel of a computer apparatus, and a rear camera is disposed on a rear surface of the computer apparatus. In some embodiments, the number of the rear cameras is at least two, and each rear camera is any one of a main camera, a depth-of-field camera, a wide-angle camera and a telephoto camera, so that the main camera and the depth-of-field camera are fused to realize a background blurring function, the main camera and the wide-angle camera are fused to realize panoramic shooting and a VR (Virtual Reality) shooting function or other fusion shooting functions. In some embodiments, camera assembly 1606 can also include a flash. The flash lamp can be a monochrome temperature flash lamp or a bicolor temperature flash lamp. The double-color-temperature flash lamp is a combination of a warm-light flash lamp and a cold-light flash lamp and can be used for light compensation under different color temperatures.

The audio circuitry 1607 may include a microphone and a speaker. The microphone is used for collecting sound waves of a user and the environment, converting the sound waves into electric signals, and inputting the electric signals to the processor 1601 for processing or inputting the electric signals to the radio frequency circuit 1604 for voice communication. The microphones may be multiple, each located at a different location on the computer device 1600 for stereo sound capture or noise reduction purposes. The microphone may also be an array microphone or an omni-directional acquisition microphone. The speaker is used to convert electrical signals from the processor 1601 or the radio frequency circuit 1604 into sound waves. The loudspeaker can be a traditional film loudspeaker or a piezoelectric ceramic loudspeaker. When the speaker is a piezoelectric ceramic speaker, the speaker can be used for purposes such as converting an electric signal into a sound wave audible to a human being, or converting an electric signal into a sound wave inaudible to a human being to measure a distance. In some embodiments, the audio circuit 1607 may also include a headphone jack.

The Location component 1608 is employed to locate a current geographic Location of the computer device 1600 for purposes of navigation or LBS (Location Based Service). The Positioning component 1608 may be a Positioning component based on the Global Positioning System (GPS) in the united states, the beidou System in china, or the galileo System in russia.

Power supply 1609 is used to power the various components within computer device 1600. Power supply 1609 may be alternating current, direct current, disposable or rechargeable. When power supply 1609 includes a rechargeable battery, the rechargeable battery may be a wired rechargeable battery or a wireless rechargeable battery. The wired rechargeable battery is a battery charged through a wired line, and the wireless rechargeable battery is a battery charged through a wireless coil. The rechargeable battery may also be used to support fast charge technology.

In some embodiments, computer device 1600 also includes one or more sensors 1610. The one or more sensors 1610 include, but are not limited to: acceleration sensor 1611, gyro sensor 1612, pressure sensor 1613, fingerprint sensor 1614, optical sensor 1615, and proximity sensor 1616.

The acceleration sensor 1611 may detect acceleration magnitudes on three coordinate axes of a coordinate system established with the computer apparatus 1600. For example, the acceleration sensor 1611 may be used to detect components of the gravitational acceleration in three coordinate axes. The processor 1601 may control the display 1605 to display the user interface in a landscape view or a portrait view according to the gravitational acceleration signal collected by the acceleration sensor 1611. The acceleration sensor 1611 may also be used for acquisition of motion data of a game or a user.

Gyroscope sensor 1612 can detect computer device 1600's organism direction and turned angle, and gyroscope sensor 1612 can gather the 3D action of user to computer device 1600 in coordination with acceleration sensor 1611. From the data collected by the gyro sensor 1612, the processor 1601 may perform the following functions: motion sensing (such as changing the UI according to a user's tilting operation), image stabilization at the time of photographing, game control, and inertial navigation.

The pressure sensors 1613 may be disposed on the side bezel of the computer device 1600 and/or underneath the display 1605. When the pressure sensor 1613 is disposed on the side frame of the computer device 1600, the holding signal of the user to the computer device 1600 can be detected, and the processor 1601 performs left-right hand recognition or shortcut operation according to the holding signal collected by the pressure sensor 1613. When the pressure sensor 1613 is disposed at the lower layer of the display screen 1605, the processor 1601 controls the operability control on the UI interface according to the pressure operation of the user on the display screen 1605. The operability control comprises at least one of a button control, a scroll bar control, an icon control and a menu control.

The fingerprint sensor 1614 is configured to collect a fingerprint of the user, and the processor 1601 is configured to identify the user based on the fingerprint collected by the fingerprint sensor 1614, or the fingerprint sensor 1614 is configured to identify the user based on the collected fingerprint. Upon recognizing that the user's identity is a trusted identity, the processor 1601 authorizes the user to perform relevant sensitive operations including unlocking a screen, viewing encrypted information, downloading software, paying for and changing settings, etc. Fingerprint sensor 1614 may be disposed on the front, back, or side of computer device 1600. When a physical button or vendor Logo is provided on the computer device 1600, the fingerprint sensor 1614 may be integrated with the physical button or vendor Logo.

The optical sensor 1615 is used to collect ambient light intensity. In one embodiment, the processor 1601 may control the display brightness of the display screen 1605 based on the ambient light intensity collected by the optical sensor 1615. Specifically, when the ambient light intensity is high, the display brightness of the display screen 1605 is increased; when the ambient light intensity is low, the display brightness of the display screen 1605 is adjusted down. In another embodiment, the processor 1601 may also dynamically adjust the shooting parameters of the camera assembly 1606 based on the ambient light intensity collected by the optical sensor 1615.

A proximity sensor 1616, also known as a distance sensor, is typically disposed on a front panel of the computer device 1600. The proximity sensor 1616 is used to capture the distance between the user and the front of the computer device 1600. In one embodiment, the display 1605 is controlled by the processor 1601 to switch from a bright screen state to a dark screen state when the proximity sensor 1616 detects that the distance between the user and the front surface of the computer device 1600 is gradually decreasing; the display 1605 is controlled by the processor 1601 to switch from a rest state to a lighted state when the proximity sensor 1616 detects that the distance between the user and the front surface of the computer device 1600 is gradually increasing.

Those skilled in the art will appreciate that the configuration shown in FIG. 16 is not intended to be limiting of computer device 1600, and may include more or fewer components than those shown, or some components may be combined, or a different arrangement of components may be employed.

The embodiment of the present application further provides a computer-readable storage medium, where at least one program code is stored in the computer-readable storage medium, and the at least one program code is loaded and executed by a processor to implement the operations performed in the method for predicting interlayer distribution in the embodiment of the present application.

Embodiments of the present application also provide a computer program product or a computer program comprising computer program code, the computer program code being stored in a computer readable storage medium. A processor of the computer device reads the computer program code from the computer-readable storage medium, and the processor executes the computer program code to cause the computer device to perform the operations performed by the interlayer distribution prediction method described above.

In some embodiments, the computer program according to the embodiments of the present application may be deployed to be executed on one computer device or on multiple computer devices located at one site, or may be executed on multiple computer devices distributed at multiple sites and interconnected by a communication network, and the multiple computer devices distributed at the multiple sites and interconnected by the communication network may constitute a block chain system.

The above description is only for facilitating the understanding of the technical solutions of the present application by those skilled in the art, and is not intended to limit the present application. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method of predicting interlayer distribution, the method comprising:

determining a first distribution training image based on the interlayer information of the multiple types of interlayers, wherein the first distribution training image is used for predicting the distribution of braided river reservoir interlayers;

acquiring interlayer distribution data of multiple types of interlayers, wherein the interlayer distribution data are the interlayer distribution data of each single well in the drilling process of multiple single wells drilled in the work area;

and predicting the spatial distribution of the interlayers of various types in the work area based on the first distribution training image, the interlayer distribution data and the distribution probability image.

2. The method of claim 1, wherein determining a first distribution training image based on the band information for the multiple types of bands comprises:

3. The method of claim 2, wherein said integrating the distribution training subimages of each type of band into one image resulting in the first distribution training image comprises:

integrating multiple types of interlayers into one image based on the position of each type of interlayer in the distribution training subimage of each type of interlayer;

and under the condition that multiple types of interlayers appear at the same position, taking the interlayer with high priority as the interlayer corresponding to the position based on the priority of each type of interlayer to obtain the first distribution training image.

4. The method of claim 2, wherein said integrating the distribution training subimages of each type of band into one image resulting in the first distribution training image comprises:

5. The method of claim 1, wherein determining a distribution probability image for each type of interbed within the work area based on the attribute information for the target seismic attribute and the interbed distribution data comprises:

determining a correlation between the distribution probability of each type of interlayer at different positions in a first area and the attribute information of the target seismic attribute based on the interlayer distribution data, wherein the first area is the area where the drilled single wells are located;

and integrating the distribution probability of each position and each type of interlayer corresponding to the position in the second region and the distribution probability of each position and each type of interlayer corresponding to the position in the first region into one image based on the size of the region of the work area to obtain the distribution probability image.

6. The method of claim 1, wherein predicting the spatial distribution of multiple types of interlayers within the work area based on the first distribution training image, the interlayer distribution data, and the distribution probability image comprises:

randomly determining a current prediction position in the work area, and determining a first distribution probability of each type of interlayer corresponding to the current prediction position based on the first distribution training image, the positions of the drilled single wells and each previous prediction position, wherein the first distribution probability is an interlayer distribution probability constrained by a geological deposition and development law;

7. The method of claim 6, wherein determining a first distribution probability for each type of interval corresponding to the current predicted location based on the first distribution training image, the locations of the drilled plurality of individual wells, and each previously predicted location comprises:

8. An apparatus for predicting interlayer distribution, the apparatus comprising:

the second determination module is used for determining a distribution probability image of each type of interlayer in the work area based on the attribute information of the target seismic attribute and the interlayer distribution data;

and the prediction module is used for predicting the spatial distribution of the interlayers of various types in the work area based on the first distribution training image, the interlayer distribution data and the distribution probability image.

9. The apparatus of claim 8, wherein the first determining module is configured to determine a distribution training sub-image for each type of interlayer based on the interlayer information for each type of interlayer; and integrating the distribution training subimages of each type of interlayer into one image to obtain the first distribution training image.

10. A computer device, characterized in that it comprises a processor and a memory, in which at least one program code is stored, which is loaded and executed by the processor, to implement the interlayer distribution prediction method of any of claims 1 to 7.