CN109389154B - Method and device for identifying lithofacies of conglomerate reservoir of flood fan - Google Patents
Method and device for identifying lithofacies of conglomerate reservoir of flood fan Download PDFInfo
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Abstract
The embodiment of the application provides a method and a device for identifying a conglomerate reservoir lithofacies of a flood fan, wherein the method comprises the following steps: determining the deposition characteristics of the fan top of the flood fan; identifying deposition microphases in the fan top of the flood fan according to the deposition characteristics; forming rock phase sample data according to the data of the coring wells in the fan tops of the flood fan; training a sample by using a part of lithofacies sample data, and constructing lithofacies recognition models of different lithofacies in sedimentary microfacies by using a Bayes discrimination method; the lithofacies identification model comprises a corresponding relation between specific reservoir parameters and lithofacies; verifying whether the lithofacies identification model meets preset requirements or not based on the rest lithofacies sample data; and when the lithofacies recognition model meets the preset requirement, obtaining specific reservoir parameter values of the target layer rock in the sedimentary microfacies, and substituting the reservoir parameter values into the lithofacies recognition model to obtain the lithofacies type of the target layer rock. The method and the device can improve the identification accuracy and the identification efficiency of the rock facies of the conglomerate reservoir of the flood fan, and provide a basis for fine evaluation of the conglomerate reservoir at the top of the flood fan.
Description
Technical Field
The application relates to the technical field of petroleum and natural gas exploration and development, in particular to a method and a device for identifying a conglomerate reservoir lithofacies of a flood fan.
Background
The Hongda fan belongs to the alluvial fan of seasonal rainstorm cause under the arid or semiarid climate condition. Flood carries a large amount of clastic substances to flow out of a mountain mouth, and due to the gradual gradient, the reduction of flow rate and the dispersion of water flow, and evaporation or downward permeation, the clastic substances are quickly accumulated, the sediments are mainly gravels supported by gravels, the separation is extremely poor, the roundness grinding is low, the sediments are mainly sub-round-sub-angular, the components are complex, the impurity content is high, and the sediments are a special gravels reservoir formed under the near-source high-energy environment.
At present, the research on the flood fan is mainly carried out by carrying out a large amount of analysis and discussion on modern deposition, field outcrop and coring data so as to establish a deposition mode of the flood fan, and qualitatively identifying and dividing deposition microphase according to the data such as deposition characteristics, curve modes and the like. However, due to the extremely strong heterogeneity of the reservoir, the relationship between the sedimentary microfacies of the reservoir and the reservoir quality is not clear, the distribution of different lithofacies is not uniform in the same sedimentary microfacies type, the reservoir quality is often greatly different, the traditional sedimentary microfacies research scale cannot meet the evaluation requirement of the glutenite reservoir, and a technical scheme for accurately identifying the spatial distribution of the glutenite reservoir of the flood fan is urgently needed at present.
Disclosure of Invention
An object of the embodiments of the present application is to provide a method and an apparatus for identifying a petrographic facies of a conglomerate reservoir of a flood fan, so as to improve accuracy and identification efficiency of identifying the petrographic facies of the conglomerate reservoir of the flood fan.
In order to achieve the above object, in one aspect, an embodiment of the present application provides a method for identifying a petrographic phase of a conglomerate reservoir of a flood fan, including:
determining the deposition characteristics of the fan top of the flood fan;
identifying sedimentary microfacies in the fan top of the flood fan according to the sedimentary features;
forming rock phase sample data according to the data of the coring wells in the fan tops of the flood fan;
training a sample by using a part of lithofacies sample data, and constructing lithofacies recognition models of different lithofacies in the sedimentary microfacies by using a Bayesian discrimination method; the lithofacies identification model comprises a corresponding relation between specific reservoir parameters and lithofacies;
verifying whether the lithofacies identification model meets preset requirements or not based on the rest lithofacies sample data;
and when the lithofacies recognition model meets the preset requirement, obtaining specific reservoir parameter values of the target layer rock in the sedimentary microfacies, and substituting the reservoir parameter values into the lithofacies recognition model to obtain the lithofacies type of the target layer rock.
The method for identifying the glutenite reservoir lithofacies of the flood fan further comprises the following steps:
After the lithofacies type of the target layer rock is obtained, determining the combination and the proportion of the lithofacies in the same small layer by taking the small layer as a unit;
determining the planar distribution of lithofacies of the small layers in the sedimentary microfacies based on the combination and the proportion of the lithofacies in the same small layer.
According to the method for identifying the glutenite reservoir lithofacies of the flood fan, the deposition characteristics of the fan top of the flood fan comprise lithology characteristics, granularity distribution characteristics and deposition structure characteristics.
The method for identifying the lithofacies of the conglomerate reservoir of the flood fan comprises the following steps of:
performing single-well facies observation analysis on cores of coring wells in the fan tops of the flood fan to count the value ranges of specific reservoir parameters corresponding to different facies; and the values of different lithofacies and corresponding specific reservoir parameters form lithofacies sample data.
The method for identifying the lithofacies of the conglomerate reservoir of the flood fan is characterized in that specific reservoir parameter values of the rocks of the target layer in the sedimentary microfacies are obtained and substituted into the lithofacies identification model to obtain the lithofacies types of the rocks of the target layer, and the method comprises the following steps:
Respectively substituting the obtained specific reservoir stratum parameter values of the target stratum rock in the sedimentary microfacies into each lithofacies recognition model to correspondingly obtain a plurality of calculated values;
and determining the maximum one of the calculated values, and giving the lithofacies type corresponding to the maximum one to the target layer rock. The method for identifying the lithofacies of the conglomerate reservoir of the flood fan in the embodiment of the application is characterized in that whether the lithofacies identification model meets the preset requirement or not is verified based on the rest lithofacies sample data, and the method comprises the following steps:
and verifying whether the identification accuracy of the lithofacies identification model is greater than a preset accuracy threshold value or not based on the rest lithofacies sample data.
The method for identifying the lithofacies of the conglomerate reservoir of the flood fan comprises the following steps of: a conglomerate lithofacies recognition model, a glutenite lithofacies recognition model, a sandstone lithofacies recognition model and a mudstone lithofacies recognition model.
On the other hand, this application embodiment still provides a device is discerned to flood fan glutenite reservoir lithofacies, includes:
the sedimentation characteristic determining module is used for determining the sedimentation characteristics of the fan top of the flood fan;
the deposition microfacies determining module is used for identifying the deposition microfacies in the fan top of the flood fan according to the deposition characteristics;
The sample data acquisition module is used for forming rock phase sample data according to the core well data in the fan top of the flood fan;
the identification model construction module is used for constructing lithofacies identification models of different lithofacies in the sedimentary microfacies by using a part of lithofacies sample data training samples and utilizing a Bayesian discrimination method; the lithofacies identification model comprises a corresponding relation between specific reservoir parameters and lithofacies;
the recognition model checking module is used for verifying whether the lithofacies recognition model meets the preset requirement or not based on the rest lithofacies sample data;
and the lithofacies type identification module is used for acquiring specific reservoir parameter values of the target layer rock in the sedimentary microfacies when the lithofacies identification model meets the preset requirement, and substituting the reservoir parameter values into the lithofacies identification model to acquire the lithofacies type of the target layer rock.
The device for identifying the glutenite reservoir lithofacies of the flood fan further comprises:
the lithofacies distribution determining module is used for determining the combination and the proportion of lithofacies in the same small layer by taking the small layer as a unit after the lithofacies type of the rock of the target layer is obtained; and determining the planar distribution of the facies in the sedimentary microfacies according to the combination and the proportion of the facies in the same small layer.
On the other hand, the embodiment of the present application further provides another apparatus for identifying a conglomerate reservoir lithofacies of a flood sector, including a memory, a processor, and a computer program stored on the memory, where the computer program, when executed by the processor, performs the following steps:
determining the deposition characteristics of the fan top of the flood fan;
identifying sedimentary microfacies in the fan top of the flood fan according to the sedimentary features;
forming rock phase sample data according to the data of the coring wells in the fan tops of the flood fan;
training a sample by using a part of lithofacies sample data, and constructing lithofacies recognition models of different lithofacies in the sedimentary microfacies by using a Bayesian discrimination method; the lithofacies identification model comprises a corresponding relation between specific reservoir parameters and lithofacies;
verifying whether the lithofacies identification model meets preset requirements or not based on the rest lithofacies sample data;
and when the lithofacies recognition model meets the preset requirement, obtaining specific reservoir parameter values of the target layer rock in the sedimentary microfacies, and substituting the reservoir parameter values into the lithofacies recognition model to obtain the lithofacies type of the target layer rock.
According to the technical scheme provided by the embodiment of the application, on the basis of identifying the sedimentary microfacies in the fan top of the flood fan according to the sedimentary characteristics, lithofacies sample data can be formed according to the data of the coring wells in the fan top of the flood fan; then, constructing a lithofacies recognition model of different lithofacies in the sedimentary microfacies based on a part of lithofacies sample data; the lithofacies identification model comprises a corresponding relation between specific reservoir parameters and lithofacies; verifying whether the lithofacies identification model meets the preset requirements or not based on the rest lithofacies sample data; and when the lithofacies identification model meets the preset requirement, acquiring specific reservoir parameter values of the target layer rock in the sedimentary microfacies, substituting the reservoir parameter values into the lithofacies identification model to obtain the lithofacies type of the target layer rock, and thus realizing identification of the conglomerate reservoir lithofacies of the Hongji fan according to the lithofacies identification model and the specific reservoir parameter values. Compared with the traditional method of qualitatively identifying the lithology by mainly based on subjective contrast of certain lithology, the lithology identification method and the lithology identification device can identify the lithology of the divided sedimentary microfacies according to the lithology identification model and specific reservoir parameters, and are more objective, more detailed, visual and faster in identification, so that the accuracy and the identification efficiency of identifying the lithology of the conglomerate reservoir of the Hongyama fan are improved.
Drawings
In order to more clearly illustrate the embodiments of the present application 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 application, 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 flowchart of a method for identifying a conglomerate reservoir lithofacies of a flood fan in an embodiment of the present application;
FIG. 2a is a triangular view of the composition of conglomerate rock according to an embodiment of the present application;
figure 2b is a triangular view of sandstone components in an embodiment of the present application;
FIG. 3 is a schematic diagram of a single well sedimentary facies division in an embodiment of the present application;
FIGS. 4a to 4f are enlarged schematic views of the particle size analysis section in the single well sedimentary facies partition diagram of FIG. 3;
FIGS. 5 a-5 f are schematic views of the main deposition structure of the fan roof of the flood fan in one embodiment of the present application;
FIG. 6 is a schematic view of a deposition microphase plan profile in an embodiment of the present application;
FIG. 7 is a sedimentary microfacies-lithofacies plane distribution plot in an embodiment of the present application;
fig. 8 is a block diagram illustrating a structure of a glutenite reservoir lithofacies identification apparatus of the flood fan in an embodiment of the present application;
Fig. 9 is a block diagram of a device for identifying lithofacies of a conglomerate reservoir of a flooding fan in another embodiment of the present application.
Detailed Description
In order to make those skilled in the art better understand the technical solutions in the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
Referring to fig. 1, a method for identifying a glutenite reservoir lithofacies of a flood fan according to an embodiment of the present application may include the following steps:
s101, determining the deposition characteristics of the fan top of the flood fan.
In one embodiment of the application, the formation of the flood fans is not only controlled by seasonal rainstorms, but also restricted by the topography and landform of the development area. Moreover, deposition characteristics of the flood fans formed in different regions often vary greatly, and it is necessary to confirm the top deposition characteristics of the flood fans in the research region by analyzing lithology characteristics, particle size distribution characteristics and deposition structure characteristics of the reservoir before carrying out the deposition microphase research.
In one embodiment of the present application, conglomerate component triangulation maps (e.g., as shown in FIG. 2 a) and sandstone component triangulation maps (e.g., as shown in FIG. 2 b) may be generated based on the identification statistics of the core slices of the cored wells using component triangulation analysis methods, such that percentages of various lithologies and mineral contents may be determined from the component triangulation maps.
In an embodiment of the present application, a particle size probability curve distribution diagram (for example, shown in fig. 4a to 4 f) of each core well segment can be respectively generated based on the statistical data of the particle size analysis of the core well and by using a particle size probability curve analysis method, so as to clearly study the hydrodynamic mechanism of the carried sediment in the area.
In one embodiment of the present application, the depositional structure is an important mark for restoring the ancient depositional environment, and is a direct reflection of hydrodynamic conditions during sediment deposition, so that the depositional structure has good phase-indicating performance, and various depositional structures (such as shown in fig. 5a to 5 f) can be recognized through core image observation of a core well, so as to identify the ancient depositional environment of a research area. Wherein, fig. 5a is an image of pebble conglomerate; FIG. 5b is an image of a flooding hierarchy; figure 5c is an image of parallel bedding (fine sand); FIG. 5d is an image of the directional arrangement of conglomerate on the washout surface; fig. 5e is an image of a core in an oblique hierarchy and fig. 5f is an image of a grain-sequential hierarchy.
S102, identifying the deposition microphase in the fan top of the flood fan according to the deposition characteristics.
In an embodiment of the present application, the partition of the depositional microfacies of the ceiling of the flood fan can be performed according to the depositional characteristics of the ceiling of the flood fan, the well log curve, and other data, and in an exemplary embodiment of the present application, the partition of the depositional microfacies of the ceiling of the flood fan can be, for example, as shown in fig. 3 and table 1 below.
TABLE 1
As can be seen from table 1, the sedimentary microfacies of the fan roof of the flood fan can include main troughs, troughs beaches, and flood zones. Wherein, the main tank and the tank beach have better oil-containing property. Most flood zones are mudstones, but some sandstone and fine sandstone exist, and the flood zones are considered to contain no oil or have poor oil-containing property.
In an embodiment of the present application, on the basis of identifying the deposition microphases in the ceiling of the flood fan, a planar distribution map of the deposition microphases can be drawn, for example, as shown in fig. 6.
And S103, forming lithofacies sample data according to the core well data in the fan top of the flood fan.
In an embodiment of the application, single-well facies observation analysis can be performed on cores of core wells in the top of the flood sector to count the value ranges of specific reservoir parameters corresponding to different facies. And the values of different lithofacies and corresponding specific reservoir parameters form lithofacies sample data. In an exemplary embodiment of the present application, the value ranges of the specific reservoir parameters corresponding to different lithofacies may be, for example, as shown in table 2 below.
In an embodiment of the present application, the specific reservoir parameters may be reservoir parameters that reflect each lithofacies extracted from a plurality of typical samples and then have a main role in classification of lithofacies in the local region through stepwise discriminant analysis. In an exemplary embodiment, as can be seen in table 2, the specified reservoir parameters may include acoustic moveout, resistivity, porosity, permeability, shale content, and median particle size.
TABLE 2
S104, training a sample by using a part of lithofacies sample data, and constructing lithofacies recognition models of different lithofacies in the sedimentary microfacies by using a Bayes discrimination method; wherein the lithofacies identification model comprises a correspondence of specific reservoir parameters to lithofacies.
In an embodiment of the application, a part of lithofacies sample data training samples can be used, and a Bayes (Bayes) discrimination method is utilized to construct lithofacies recognition models of different lithofacies in the depositional microfacies. The Bayes judgment method has the following principle:
firstly, reservoir parameters which play a main role in the lithofacies classification of the region are selected preferably, standard samples are grouped, input files of discriminant analysis are established, and a Bayes discriminant analysis method is utilized to establish lithofacies recognition models of various reservoirs. The general formula is as follows:
The lithofacies recognition model of the ith lithofacies is as follows:
Pi＝α0i+∑α0iXij,i＝1,2,3,4；j＝1,2,...11.
wherein, alpha is a discrimination coefficient, XijIs the j characteristic variable, P, of the facies of the i categoryiAnd the reservoir to be judged belongs to the discrimination value of the reservoir of the ith type.
The criteria for Bayes discriminant analysis are: assuming that it is equally probable that the sample is from the various classes of parents, the posterior probability P of the sample from class i is calculated by Bayes' formulai：
Wherein, Pi(y1...ym) As sample Y (Y)1...ym) Probability density, q, belonging to group i1Is the prior probability of the ith group. The frequency of the sample can be used as an estimate of the prior probability, i.e. q1＝n1N, the criterion for judging the sample attribution type is Pi(y1...ym) Is the largest.
When the lithofacies recognition model is equal to
Then, a new sample Y (Y)1...ym) Enter category S.
In an exemplary embodiment of the application, a Bayes discriminant analysis method is used to establish lithofacies recognition models of different lithofacies in sedimentary microfacies:
a conglomerate phase identification model:
Y＝966.944*Ф-153.246*K-2.725*Md-0.210*Vsh+240.684*RT-2905.827
a gravel lithofacies identification model:
Y＝951.834*Ф-151.696*K-1.436*Md-0.071*Vsh+236.851*RT-2814.702
③ sandstone phase identification model:
Y＝856.504*Ф-136.818*K-0.248*Md+0.812*Vsh+220.030*RT-2356.630
fourthly, identifying the mudstone facies model:
where Φ is porosity, K is permeability, Md is median particle size, Vsh is shale content, RT is resistivity, and AC is acoustic time difference.
In an embodiment of the present application, since the reservoir properties of different lithofacies are different, it is advantageous to find the best reservoir by establishing lithofacies identification models of different lithofacies.
And S105, verifying whether the lithofacies identification model meets preset requirements or not based on the rest lithofacies sample data.
In an embodiment of the present application, in order to verify or verify the performance of the lithofacies identification model constructed in S104, it may be verified whether the lithofacies identification model meets a preset requirement based on the remaining lithofacies samples. For example, for the conglomerate phase identification model, the conglomerate phase data in the remaining rock phase sample can be input as a model, and then the identification performance of the conglomerate phase identification model can be judged according to the output result. In an embodiment of the present application, the verifying whether the lithofacies identification model meets a preset requirement may be, for example, verifying whether an identification accuracy of the lithofacies identification model is greater than a preset accuracy threshold; if the number of the lithofacies identification models is larger than the preset number, the identification performance of the lithofacies identification models is accurate and reliable. Otherwise, the facies identification model may be further optimized (e.g., coefficients adjusted, data samples adjusted, etc.) to meet the requirements.
S106, when the lithofacies recognition model meets preset requirements, obtaining specific reservoir parameter values of the target layer rock in the sedimentary microfacies, and substituting the reservoir parameter values into the lithofacies recognition model to obtain the lithofacies type of the target layer rock.
In an embodiment of the present application, the obtaining of the specific reservoir parameter value of the target layer rock in the sedimentary microfacies and substituting the specific reservoir parameter value into the lithofacies recognition model to obtain the lithofacies type of the target layer rock specifically includes: respectively substituting the obtained specific reservoir parameter values of the target layer rock in the sedimentary microfacies into each lithofacies identification model (such as the conglomerate facies identification model, the gravel lithofacies identification model, the sandstone facies identification model and the mudstone facies identification model), so as to correspondingly obtain a plurality of Y values; and determining the maximum one of the Y values, and giving the lithofacies type corresponding to the maximum one to the target layer rock.
In an embodiment of the present application, the fact that the lithofacies recognition models meet preset requirements means that the lithofacies recognition models of different lithofacies in the depositional microfacies all meet preset requirements. In this case, it can be used for lithofacies of different lithofacies within the non-cored well region where the microfacies are deposited. In general, sandstone in a target zone is further classified into conglomerate, sandstone, and the like, and the properties of various lithologies are different, and these lithologies are mixed. Compared with the traditional method which mainly bases on the ratio of certain lithology to the traditional method and qualitatively identifying the lithology. According to the embodiment of the application, lithofacies identification can be carried out on the sedimentary microfacies according to the lithofacies identification model and specific reservoir parameters, and the lithofacies identification model is more objective, more detailed, and more visual and faster.
In an embodiment of the present application, after step S106, the combination and the proportion of lithofacies in the same small layer may also be determined by taking the small layer as a unit; then, based on the combination and the proportion of the lithofacies in the same small layer, the lithofacies plane distribution of the small layer in the sedimentary microfacies is determined, for example, as shown in fig. 7. Generally, the target stratum has stratum thickness, the thickness of various lithological characters in the small stratum is counted, and the percentage of the thickness of various lithological characters in the thickness of the small stratum can be calculated.
Referring to fig. 8, a device for identifying a conglomerate reservoir lithofacies of a flood fan according to an embodiment of the present application may include:
a deposition characteristic determining module 81, configured to determine a deposition characteristic of a ceiling of a flood fan;
a depositional microfacies determination module 82 operable to identify depositional microfacies within the fan roof of the flood fan based on the depositional characteristics;
the sample data acquisition module 83 may be configured to form rock phase sample data according to the core well data in the top of the flood fan;
the identification model construction module 84 may be configured to construct a lithofacies identification model of different lithofacies in the depositional microfacies by using a bayes discrimination method on a part of lithofacies sample data training samples; the lithofacies identification model comprises a corresponding relation between specific reservoir parameters and lithofacies;
The recognition model checking module 85 can be used for verifying whether the lithofacies recognition model meets the preset requirements based on the rest lithofacies sample data;
the lithofacies type identification module 86 may be configured to obtain a specific reservoir parameter value of the target layer rock in the sedimentary microfacies when the lithofacies identification model meets a preset requirement, and substitute the reservoir parameter value into the lithofacies identification model to obtain the lithofacies type of the target layer rock.
In another embodiment of the present application, the apparatus for identifying lithofacies of a conglomerate reservoir in a flood fan may further include a lithofacies distribution determining module, configured to determine a combination and a proportion of lithofacies in a same small layer by taking the small layer as a unit after obtaining the lithofacies type of the rock of the target layer; and determining the planar distribution of the facies in the sedimentary microfacies according to the combination and the proportion of the facies in the same small layer.
Referring to fig. 9, a device for identifying a conglomerate reservoir lithofacies of a flood fan according to an embodiment of the present application may include a memory, a processor, and a computer program stored on the memory, the computer program being executed by the processor to perform the following steps:
determining the deposition characteristics of the fan top of the flood fan;
Identifying sedimentary microfacies in the fan top of the flood fan according to the sedimentary features;
forming rock phase sample data according to the data of the coring wells in the fan tops of the flood fan;
training a sample by using a part of lithofacies sample data, and constructing lithofacies recognition models of different lithofacies in the sedimentary microfacies by using a Bayesian discrimination method; the lithofacies identification model comprises a corresponding relation between specific reservoir parameters and lithofacies;
verifying whether the lithofacies identification model meets preset requirements or not based on the rest lithofacies sample data;
and when the lithofacies recognition model meets the preset requirement, obtaining specific reservoir parameter values of the target layer rock in the sedimentary microfacies, and substituting the reservoir parameter values into the lithofacies recognition model to obtain the lithofacies type of the target layer rock.
While the process flows described above include operations that occur in a particular order, it should be appreciated that the processes may include more or less operations that are performed sequentially or in parallel (e.g., using parallel processors or a multi-threaded environment).
For convenience of description, the above devices are described as being divided into various units by function, and are described separately. Of course, the functionality of the units may be implemented in one or more software and/or hardware when implementing the present application.
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.
The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
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, 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, 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, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application 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.
The application 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. The application may 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.
The above description is only an example of the present application and is not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.
Claims (10)
1. A method for identifying a conglomerate reservoir lithofacies of a flood fan is characterized by comprising the following steps:
determining the deposition characteristics of the fan top of the flood fan;
identifying sedimentary microfacies in the fan top of the flood fan according to the sedimentary features;
forming rock phase sample data according to the data of the coring wells in the fan tops of the flood fan;
training a sample by using a part of lithofacies sample data, and constructing lithofacies recognition models of different lithofacies in the sedimentary microfacies by using a Bayesian discrimination method; the lithofacies identification model comprises a corresponding relation between specific reservoir parameters and lithofacies;
Verifying whether the lithofacies identification model meets preset requirements or not based on the rest lithofacies sample data;
when the lithofacies recognition model meets preset requirements, obtaining specific reservoir parameter values of the target layer rock in the sedimentary microfacies, and substituting the reservoir parameter values into the lithofacies recognition model to obtain the lithofacies type of the target layer rock;
wherein the general formula of the lithofacies recognition model is as follows:
the lithofacies recognition model of the ith lithofacies is as follows:
Pi＝α0i+∑α0iXij,i＝1,2,3,4；j＝1,2,...11.
wherein, alpha is a discrimination coefficient, XijIs the j characteristic variable, P, of the facies of the i categoryiAnd the reservoir to be judged belongs to the discrimination value of the reservoir of the ith type.
2. The method of identifying a conglomerate reservoir lithofacies of a flood fan of claim 1, further comprising:
after the lithofacies type of the target layer rock is obtained, determining the combination and the proportion of the lithofacies in the same small layer by taking the small layer as a unit;
determining the planar distribution of lithofacies of the small layers in the sedimentary microfacies based on the combination and the proportion of the lithofacies in the same small layer.
3. The method of identifying a conglomerate reservoir lithofacies of a flood fan of claim 1, wherein the sedimentary features of the fan roof of the flood fan include petrophysical features, size distribution features, and sedimentary formation features.
4. The method of identifying a petrographic phase of a conglomerate reservoir of a flood sector fan-roof of claim 1, wherein said forming lithographic phase sample data from core well data of the fan-roof of the flood sector includes:
performing single-well facies observation analysis on cores of coring wells in the fan tops of the flood fan to count the value ranges of specific reservoir parameters corresponding to different facies; and the values of different lithofacies and corresponding specific reservoir parameters form lithofacies sample data.
5. The method for identifying the lithofacies of the conglomerate reservoir of the Hongyuan fan according to claim 1, wherein the step of obtaining the specific reservoir parameter values of the target layer rocks in the sedimentary microfacies and substituting the reservoir parameter values into the lithofacies identification model to obtain the lithofacies types of the target layer rocks comprises the following steps:
respectively substituting the obtained specific reservoir stratum parameter values of the target stratum rock in the sedimentary microfacies into each lithofacies recognition model to correspondingly obtain a plurality of calculated values;
and determining the maximum one of the calculated values, and giving the lithofacies type corresponding to the maximum one to the target layer rock.
6. The method for identifying the lithofacies of the conglomerate reservoir of the flood fan according to claim 1, wherein the verifying whether the lithofacies identification model meets preset requirements based on the remaining lithofacies sample data comprises:
And verifying whether the identification accuracy of the lithofacies identification model is greater than a preset accuracy threshold value or not based on the rest lithofacies sample data.
7. The method of identifying a petrographic facies of a conglomerate reservoir of a flood fan of claim 1, wherein the model of identifying the petrographic facies of different petrographic facies within sedimentary microfacies comprises: a conglomerate lithofacies recognition model, a glutenite lithofacies recognition model, a sandstone lithofacies recognition model and a mudstone lithofacies recognition model.
8. The utility model provides a flood fan conglomerate reservoir lithofacies recognition device which characterized in that includes:
the sedimentation characteristic determining module is used for determining the sedimentation characteristics of the fan top of the flood fan;
the deposition microfacies determining module is used for identifying the deposition microfacies in the fan top of the flood fan according to the deposition characteristics;
the sample data acquisition module is used for forming rock phase sample data according to the core well data in the fan top of the flood fan;
the identification model construction module is used for constructing lithofacies identification models of different lithofacies in the sedimentary microfacies by using a part of lithofacies sample data training samples and utilizing a Bayesian discrimination method; the lithofacies identification model comprises a corresponding relation between specific reservoir parameters and lithofacies;
the recognition model checking module is used for verifying whether the lithofacies recognition model meets the preset requirement or not based on the rest lithofacies sample data;
The lithofacies type identification module is used for acquiring specific reservoir parameter values of the target layer rock in the sedimentary microfacies when the lithofacies identification model meets preset requirements, and substituting the reservoir parameter values into the lithofacies identification model to acquire the lithofacies type of the target layer rock;
wherein the general formula of the lithofacies recognition model is as follows:
the lithofacies recognition model of the ith lithofacies is as follows:
Pi＝α0i+∑α0iXij,i＝1,2,3,4；j＝1,2,...11.
wherein, alpha is a discrimination coefficient, XijIs the j characteristic variable, P, of the facies of the i categoryiAnd the reservoir to be judged belongs to the discrimination value of the reservoir of the ith type.
9. The apparatus for identifying a conglomerate reservoir lithofacies of a flood fan of claim 8, further comprising:
the lithofacies distribution determining module is used for determining the combination and the proportion of lithofacies in the same small layer by taking the small layer as a unit after the lithofacies type of the rock of the target layer is obtained; and determining the planar distribution of the facies in the sedimentary microfacies according to the combination and the proportion of the facies in the same small layer.
10. A flood fan conglomerate reservoir lithofacies identification apparatus comprising a memory, a processor, and a computer program stored on the memory, wherein the computer program when executed by the processor performs the steps of:
Determining the deposition characteristics of the fan top of the flood fan;
identifying sedimentary microfacies in the fan top of the flood fan according to the sedimentary features;
forming rock phase sample data according to the data of the coring wells in the fan tops of the flood fan;
training a sample by using a part of lithofacies sample data, and constructing lithofacies recognition models of different lithofacies in the sedimentary microfacies by using a Bayesian discrimination method; the lithofacies identification model comprises a corresponding relation between specific reservoir parameters and lithofacies;
verifying whether the lithofacies identification model meets preset requirements or not based on the rest lithofacies sample data;
when the lithofacies recognition model meets preset requirements, obtaining specific reservoir parameter values of the target layer rock in the sedimentary microfacies, and substituting the reservoir parameter values into the lithofacies recognition model to obtain the lithofacies type of the target layer rock;
wherein the general formula of the lithofacies recognition model is as follows:
the lithofacies recognition model of the ith lithofacies is as follows:
Pi＝α0i+∑α0iXij,i＝1,2,3,4；j＝1,2,...11.
wherein, alpha is a discrimination coefficient, XijIs the j characteristic variable, P, of the facies of the i categoryiAnd the reservoir to be judged belongs to the discrimination value of the reservoir of the ith type.
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| CN112926680B (en) * | 2021-03-29 | 2022-08-05 | 成都理工大学 | Microbial rock deposition microphase identification method based on Bayesian neural network |
| CN113137225A (en) * | 2021-04-12 | 2021-07-20 | 中国地质调查局油气资源调查中心 | Gravel reservoir evaluation method based on gravel component analysis |
| CN116108368B (en) * | 2023-02-21 | 2023-09-01 | 北京金阳普泰石油技术股份有限公司 | Deposition microphase identification method and device based on deep learning mixed model |
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