CN112302620B - Fracture-cavity reservoir effectiveness grading method and device combining multi-source information - Google Patents
Fracture-cavity reservoir effectiveness grading method and device combining multi-source information Download PDFInfo
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Abstract
The embodiment of the invention provides a fracture-cavity reservoir validity grading method and device combining multi-source information, wherein the method comprises the following steps: determining conventional logging data around a well, well wall electrical imaging interpretation fracture-cavity data and remote detection data beside the well of a fracture-cavity reservoir body to be evaluated; performing multi-source fusion on the well periphery conventional well logging data, the well wall electrical imaging interpretation fracture-cave data and the well side remote detection data based on a preset fusion proportion to obtain multi-source fusion data; clustering each stratum depth in the fracture-cave reservoir body based on the multi-source fusion data to obtain a plurality of clustering clusters; determining effectiveness grading results of the fracture-cavity reservoir based on the individual cluster clusters. According to the method and the device provided by the embodiment of the invention, the multi-source detection data around the well, the well wall and beside the well are fused in the grading process, and the comprehensiveness of the data enables the effectiveness grading result to be more accurate.
Description
Technical Field
The invention relates to the technical field of fracture-cavity reservoir body evaluation, in particular to a fracture-cavity reservoir body effectiveness grading method and device combining multi-source information.
Background
The fracture-cave carbonate rock reservoir is a very important oil and gas reservoir and has the characteristics of secondary fracture, erosion hole development, various reservoir types, strong heterogeneity, complex pore structure and the like. The effectiveness evaluation of the fracture-cavity reservoir body has important significance on oil and gas exploration and development, and comprises effective reservoir quality grade grading, reservoir fine logging interpretation evaluation and the like.
At present, the effectiveness analysis of the fracture-cave carbonate reservoir is mainly based on well periphery conventional well logging, well wall electrical imaging well logging and array acoustic logging, wherein the detection range of the well periphery conventional well logging is limited, the azimuth detection capability is poor, and the effectiveness evaluation effect on the strong heterogeneity reservoir is poor; the borehole wall electrical imaging logging azimuth detection capability is strong, but the detection depth is shallow, the information of the radial extension of the fracture-cavern to the stratum cannot be effectively reflected, and the limitation in the reservoir effectiveness evaluation is large. The array acoustic logging has a large detection range and strong azimuth detection capability, but further analysis and description are lacked, and related technologies are not applied to reservoir effectiveness evaluation.
Disclosure of Invention
The embodiment of the invention provides a method and a device for grading the effectiveness of a fracture-cavity reservoir in combination with multi-source information, which are used for solving the problem of poor effect of the existing method for evaluating the effectiveness of the fracture-cavity reservoir.
In a first aspect, an embodiment of the present invention provides a method for grading the effectiveness of a fracture-cavity reservoir in combination with multi-source information, including:
determining conventional logging data of the periphery of a fracture-cavity reservoir body to be evaluated, well wall electrical imaging fracture-cavity logging interpretation data and well-side sound wave remote detection imaging logging data;
performing multi-source fusion on the well periphery conventional logging data, the well wall electrical imaging fracture-hole logging interpretation data and the well side sound wave remote detection imaging logging data based on a preset fusion proportion to obtain multi-source fusion data;
clustering each stratum depth in the fracture-cave reservoir body based on the multi-source fusion data to obtain a plurality of clustering clusters;
and calculating the average value of conventional logging data and borehole wall electrical imaging fracture interpretation data corresponding to each cluster based on each cluster, and determining the effectiveness grading result of the fracture-cavity reservoir body by combining with the qualitative analysis of the well-side sound wave remote detection data.
In a second aspect, an embodiment of the present invention provides a fracture-cavity reservoir validity grading device combining multi-source information, including:
the data determining unit is used for determining well periphery conventional logging data, well wall electrical imaging fracture-cavity logging interpretation data and well side sound wave remote detection imaging logging data of a fracture-cavity reservoir body to be evaluated;
the multi-source fusion unit is used for carrying out multi-source fusion on the well periphery conventional well logging data, the well wall electric imaging fracture hole well logging interpretation data and the well side sound wave remote detection imaging well logging data based on a preset fusion proportion to obtain multi-source fusion data;
the clustering unit is used for clustering the depth of each stratum in the fracture-cave reservoir body based on the multi-source fusion data to obtain a plurality of clustering clusters;
and the grading unit is used for determining an effectiveness grading result of the fracture-cavity reservoir body based on each cluster.
In a third aspect, an embodiment of the present invention provides an electronic device, including a processor, a communication interface, a memory, and a bus, where the processor and the communication interface, the memory complete mutual communication through the bus, and the processor may call a logic command in the memory to perform the steps of the method provided in the first aspect.
In a fourth aspect, embodiments of the present invention provide a non-transitory computer readable storage medium, on which a computer program is stored, which when executed by a processor, implements the steps of the method as provided in the first aspect.
According to the method and the device for grading the effectiveness of the fracture-cavern reservoir body by combining the multi-source information, the conventional well logging data, the well wall electric imaging fracture-cavern logging interpretation data and the well side sound wave remote detection imaging logging data are subjected to multi-source fusion based on the preset fusion proportion to obtain the multi-source fusion data, the multi-source fusion data are clustered to obtain the effectiveness grading result, the well periphery, the well wall and the well side multi-source data are fused in the grading process, and the comprehensiveness of the data enables the effectiveness grading result to be more scientific and reasonable.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and those skilled in the art can also obtain other drawings according to the drawings without creative efforts.
FIG. 1 is a schematic flow chart of a fracture-cavity reservoir effectiveness grading method provided by an embodiment of the present invention;
FIG. 2 is a schematic flow chart of a fracture-cavity reservoir effectiveness classification method provided by another embodiment of the present invention;
FIG. 3 is a schematic diagram of effectiveness grading results provided by an embodiment of the present invention;
FIG. 4 is a schematic structural diagram of a fracture-cave reservoir effectiveness grading device provided by an embodiment of the invention;
fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. 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 invention.
In the existing method for analyzing the effectiveness of the fracture-cave carbonate reservoir, the radial detection range of the conventional well logging is limited, the azimuth detection capability is poor, and the effectiveness evaluation effect on the reservoir with strong heterogeneity is poor; the resistivity of the well wall or ultrasonic imaging can provide azimuth information of seam hole development, but the detection depth is shallow, generally several centimeters, the extension information of the seam hole cannot be effectively reflected, the limitation in reservoir effectiveness evaluation is large, and the requirement of effectiveness analysis of the seam hole carbonate reservoir cannot be met; the data processing method based on the array acoustic logging, particularly the cross dipole transverse wave far detection, can obtain the fracture imaging within the range of 10-50m beside a well, and can determine the azimuth and the scale of the fracture imaging, but is not applied to reservoir effectiveness evaluation due to the lack of further analysis and description.
At present, methods for carrying out reservoir effectiveness grading by applying the multi-source logging information also exist, generally, conventional logging information is utilized to construct a comprehensive indication parameter or a cross plot, but the methods are insufficient in advancement and cannot be compatible with the multi-source multi-scale information. Even, reservoir effectiveness analysis conclusions obtained by adopting different logging information have larger differences, and overall consideration cannot be achieved.
In this regard, embodiments of the present invention provide a fracture-cavity reservoir effectiveness grading method. Fig. 1 is a schematic flow chart of a fracture-cavity reservoir effectiveness grading method provided in an embodiment of the present invention, and as shown in fig. 1, the method includes:
and step 110, determining conventional well logging data, well wall electrical imaging fracture-cavity well logging interpretation data and well side acoustic remote detection imaging well logging data of the fracture-cavity reservoir body to be evaluated.
The fracture-cavity reservoir to be evaluated is the fracture-cavity reservoir needing effectiveness grading, and the fracture-cavity reservoir can be specifically a fracture-cavity carbonate reservoir.
The data from the conventional well logging of the fracture-cavity reservoir to be evaluated is obtained by conventional well logging, including but not limited to gamma logging (GR), porosity logging (CNL, DEN, AC), dual lateral resistivity logging (RLLD, RLLS), and the like. By using data obtained by conventional well logging, a series of conventional well logging interpretation results which characterize the well circumference can be obtained through calculation by an empirical formula and a theoretical model, wherein the conventional well logging interpretation results include but are not limited to shale content, porosity, permeability, mineral content and the like. The well-periphery conventional well logging interpretation data is one-dimensional data of a depth domain, and the longitudinal value interval is about 0.125 m.
The well wall electrical imaging fracture-cavity logging interpretation data of the fracture-cavity reservoir body to be evaluated are obtained through electrical imaging logging, a dynamic Image reflecting the change of the well wall resistivity is obtained through measurement and processing of an FMI (Formation Micro-scanner Image) instrument, and the well wall electrical imaging fracture-cavity logging interpretation data representing the well wall can be picked up through a statistical method on the basis, wherein the dynamic Image includes but is not limited to particle size, roundness, hole density, equivalent aperture, surface porosity and the like. The well wall electric imaging fracture-hole well logging interpretation data extracted from the electric imaging image are depth domain one-dimensional data, and the longitudinal value interval is generally 0.00254 m.
The method comprises the steps of obtaining well-side sound wave remote detection imaging logging data of a fracture-cavity reservoir body to be evaluated through sound wave remote detection, carrying out data processing on array sound wave (particularly cross dipole transverse wave) logging, and carrying out imaging analysis technologies such as data loading and conversion, data preprocessing, superposition, offset imaging and the like on reflected waves by extracting the reflected waves in a full wave train, so that the well-side sound wave remote detection imaging logging data capable of reflecting structural information such as cracks, karst caves, lithology and the like in a range of dozens of meters beside a well are obtained. The well-side acoustic remote detection imaging logging data are two-dimensional data bodies of a depth domain, and the longitudinal value interval is generally 0.1524 m.
And 120, performing multi-source fusion on the well periphery conventional well logging data, the well wall electrical imaging fracture hole well logging interpretation data and the well side acoustic remote detection imaging well logging data based on a preset fusion proportion to obtain multi-source fusion data.
Specifically, considering that the data sources of the periwell conventional logging data, the well wall electrical imaging fracture-hole logging interpretation data and the well-side sound wave remote detection imaging logging data are different, and the results obtained by respectively performing effectiveness evaluation on each data may have larger difference, so that fusion cannot be caused.
Furthermore, a fusion proportion is preset and is used for carrying out data fusion on well periphery conventional logging data, well wall electrical imaging fracture hole logging interpretation data and well side sound wave remote detection imaging logging data. Here, the preset fusion ratio is a preset fusion ratio, and the preset fusion ratio may be weights respectively corresponding to the three kinds of data at the time of fusion, and used for performing weighted fusion on the three kinds of data.
And step 130, clustering each stratum depth in the fracture-cavity reservoir body based on the multi-source fusion data to obtain a plurality of cluster clusters.
Specifically, the multi-source fusion data comprises logging data of each stratum depth in the fracture-cavity reservoir body, and a plurality of cluster clusters can be obtained by clustering the logging data of each stratum depth in the multi-source fusion data. Each cluster comprises a plurality of groups of logging data of stratum depths, and the stratums corresponding to the same cluster can be considered to belong to the same validity level. The number of clusters and the number of predetermined validity levels may be the same.
Further, the clustering algorithm adopted in step 130 may specifically be a hierarchical clustering algorithm, or may also be a K-means clustering algorithm, a density-based clustering algorithm, and the like, which is not specifically limited in the embodiment of the present invention. Preferably, a plurality of cluster clusters can be obtained by clustering from the bottom to top of the coacervate level.
And step 140, calculating the average value of the conventional logging data and the borehole wall electrical imaging fracture-cave interpretation data corresponding to each cluster based on each cluster, and determining the effectiveness grading result of the fracture-cave reservoir body by combining with the qualitative analysis of the well side sound wave remote detection data.
Specifically, effectiveness stratification is directly performed on the fracture-cavity reservoir through each cluster obtained through clustering. On the basis, the characteristics of each effectiveness grading standard on the well periphery logging, well wall imaging or well side remote detection are preset and matched with the characteristics of each cluster around the well, the well wall or the well side, so that the effectiveness grading corresponding to each cluster can be obtained, and the effectiveness grading result of the fracture-cave reservoir body is further determined. The effectiveness ranking results herein may specifically include effectiveness rankings corresponding to various formation depths in a fracture-cavity reservoir.
According to the method provided by the embodiment of the invention, multisource fusion is carried out on the well periphery conventional well logging data, the well wall electrical imaging fracture hole well logging interpretation data and the well side acoustic wave remote detection imaging well logging data based on the preset fusion proportion to obtain multisource fusion data, the multisource fusion data are clustered to obtain effectiveness grading results, the well periphery, the well wall and the well side multisource data are fused in the grading process, and the comprehensiveness of the data enables the effectiveness grading results to be more scientific and reasonable.
Based on the above embodiment, step 120 specifically includes:
respectively normalizing the well periphery conventional logging data, the well wall electrical imaging fracture-hole logging interpretation data and the well side acoustic far detection imaging logging data by taking the preset fusion proportion as a normalization constraint factor to obtain normalized well periphery conventional logging data, normalized well wall electrical imaging fracture-hole logging interpretation data and normalized well side acoustic far detection imaging logging data;
and sequentially superposing the normalized well periphery conventional logging data and the normalized well wall electrical imaging fracture-cave logging interpretation data on the normalized well side acoustic remote detection imaging logging data according to data attributes to obtain the multi-source fusion data.
Specifically, before the conventional well logging data, the borehole wall electrical imaging fracture-hole well logging interpretation data and the borehole side acoustic far detection imaging well logging data are fused, the difference of the numerical values of the conventional well logging data, the borehole wall electrical imaging fracture-hole well logging interpretation data and the borehole side acoustic far detection imaging well logging data needs to be considered. In the embodiment of the invention, the three types of data are respectively normalized to eliminate the difference of the numerical values of the three types of data. Meanwhile, considering that the three types of data have different amounts of information which can be provided in the multi-source fusion data obtained after fusion and different contribution degrees to effectiveness grading, the preset fusion proportion can be used as a constraint factor of normalization while normalization is performed, so that the three types of data obtained after normalization can be directly fused.
Here, the normalization operation using the preset fusion ratio as the normalization constraint factor may be embodied as the following formula:
wherein x is data before normalization, x min And x max For expressing the minimum and maximum values of x, λ, respectivelyAnd the preset fusion proportion corresponding to the x is obtained, and the y is normalized data.
The normalized well periphery conventional well logging data, the normalized well wall electrical imaging fracture-hole well logging interpretation data and the normalized well side sound wave far detection imaging well logging data correspond to data obtained by normalizing the well periphery conventional well logging data, the well wall electrical imaging fracture-hole well logging interpretation data and the well side sound wave far detection imaging well logging data.
After the three types of normalized data are obtained, considering that the normalized well periphery conventional logging data and the normalized well wall electrical imaging fracture-hole logging interpretation data are all one-dimensional data bodies, the normalized well side acoustic remote detection imaging logging data are two-dimensional data bodies, the normalized well periphery conventional logging data and the normalized well wall electrical imaging fracture-hole logging interpretation data are required to be sequentially superposed on the normalized well side acoustic remote detection imaging logging data according to data attributes, and the superposed multisource fusion data integrates fracture-hole characteristics contained in the conventional logging, electrical imaging and acoustic remote detection data in a two-dimensional data body mode, and is used as a clustering data set for carrying out comprehensive evaluation on a reservoir body, so that multisource data information can be mined to the maximum extent, and incompatibility of independent processing results of the multisource information is avoided.
Based on any embodiment, further comprising between step 110 and step 120:
adjusting the value interval of the well periphery conventional logging data to the value interval of the well side sound wave remote detection imaging logging data based on a linear interpolation method;
and adjusting the value interval of the well wall electrical imaging fracture-cave well logging interpretation data to the value interval of the well side sound wave remote detection imaging well logging data based on an equal interval sampling method.
Specifically, considering that the three types of data have different value intervals in the depth domain, the value intervals of the three types of data need to be unified before the three types of data are fused.
Generally, the value interval of the conventional well logging data around the well is 0.125m, the value interval of the well wall electric imaging fracture-cave well logging interpretation data is 0.00254m, and the value interval of the well side sound wave remote detection imaging well logging data is 0.1524 m. In the embodiment of the invention, the value interval of the well side sound wave remote detection imaging logging data is used as a reference, and the value interval of the well periphery conventional logging data and the well wall electrical imaging fracture-hole logging interpretation data is adjusted to be consistent with the value interval of the well side sound wave remote detection imaging logging data.
Further, considering that the value interval of the well-periphery conventional logging data is larger than that of the well-side sound wave remote detection imaging logging data, the well-periphery conventional logging data with the same value interval as that of the well-side sound wave remote detection imaging logging data can be determined in a linear interpolation mode. Considering that the value interval of the well wall electric imaging fracture-hole logging interpretation data is far smaller than that of the well-side sound wave remote detection imaging logging data, the well wall electric imaging fracture-hole logging interpretation data with the same value interval as that of the well-side sound wave remote detection imaging logging data can be obtained in an equal-interval sampling mode.
Based on any one of the embodiments, in the method, the preset fusion proportion is determined based on the number of attributes respectively contained in the well periphery conventional logging data, the well wall electrical imaging fracture-hole logging interpretation data and the well side acoustic remote detection imaging logging data.
Specifically, each data includes information of a plurality of attributes, for example, conventional well logging data includes information of well logging attributes such as mud content, porosity, permeability, mineral content, etc., well wall electrical imaging fracture-cave logging interpretation data includes information of well wall attributes such as particle size, roundness, hole density, equivalent pore size, surface porosity, etc., and well-side acoustic remote detection imaging logging data includes information of well-side attributes such as cracks, karst caves, lithology, etc.
When three kinds of data are fused, the proportion of each attribute under each kind of data in the multi-source fused data can be evenly divided. Correspondingly, the preset fusion proportion may be determined based on the number of attributes respectively contained in the well-periphery conventional logging data, the borehole-wall electrical imaging fracture-hole logging interpretation data and the well-side acoustic far-detection imaging logging data, for example, the preset fusion proportion of each attribute under each data is set as the reciprocal of the total number of attributes.
Based on any of the above embodiments, in step 130, the stratum depths of the stratum depths in the fracture-cavity reservoir are clustered, the clustering algorithm adopted is bottom-to-top aggregation level clustering, and the aggregation level clustering specifically includes:
(1) regarding each sample point in the multi-source fusion data as a class, and calculating the distance between the sample points to be clustered point by adopting Euclidean distance, wherein M represents a sample dimension, and a and b represent two sample points respectively:
in the formula, the Euclidean distance D between two sample points a and b plot Is the square of the sum of the squared differences for each dimension for the two sample points.
(2) Combining the sample points into a cluster in pairs on the principle of shortest distance;
(3) calculating the distance between clusters by using an Average Linkage method, namely calculating the mathematical expectation of the Euclidean distance between all data points in two clusters, and merging the clusters with the shortest cluster distance;
(4) and (4) repeating the step (3) until the required cluster number is reached, or all the clusters are combined into one type.
Based on any of the above embodiments, step 140 specifically includes:
comparing the attribute characteristics of any cluster with the preset attribute characteristics of a plurality of effectiveness levels to obtain the corresponding effectiveness level of the cluster;
the attribute characteristics of the cluster are determined based on the attribute of the stratum depth in the cluster, wherein the attribute is corresponding to at least one of the conventional well logging data, the borehole wall electrical imaging fracture-hole well logging interpretation data and the well side acoustic remote detection imaging well logging data of the stratum depth.
Specifically, for any cluster obtained by clustering, the attribute characteristics may be information embodied by the formation depth included in the cluster in the well-surrounding conventional well logging data, borehole wall electrical imaging fracture-hole well logging interpretation data and well-side acoustic remote detection imaging well logging data, for example, whether an obvious fracture-hole structure or structure exists at the formation depth included in the cluster.
Before effectiveness analysis is carried out, a plurality of effectiveness levels can be preset, and attribute characteristics of each effectiveness level are set for comparison with the attribute characteristics of the cluster during subsequent effectiveness analysis. And if the attribute characteristics of any cluster are consistent with those of any validity level, determining that the cluster is matched with the validity level, wherein all stratum depths contained in the cluster correspond to the validity level.
Based on any of the above embodiments, the preset multiple effectiveness levels include a class I reservoir, a class II reservoir, a class III reservoir, and a dry layer, where the class I reservoir and the class II reservoir are effective reservoirs, and the class III reservoir and the dry layer are ineffective reservoirs.
Specifically, the effectiveness level may be divided into four levels in advance, and the levels correspond to a class I reservoir, a class II reservoir, a class III reservoir, and a dry layer, respectively. In the I-type reservoir, sound wave far detection imaging in the near-well sound wave far detection imaging logging data shows high-angle cracks, a strong reflection acoustic impedance interface is interpreted as a vertical crack in a carbonate rock stratum, and secondary cracks with weak reflection signal amplitude are distributed near the cracks with strong signal amplitude; the acoustic wave far detection imaging in the acoustic wave far detection imaging logging data of the II type reservoir stratum, which displays a crack-hole type reservoir stratum, wherein holes develop along cracks to form reservoir bodies which are communicated continuously; in the III-type reservoir, the acoustic wave far detection imaging in the acoustic wave far detection imaging logging data beside the well displays the cave development, and the cave is easy to be filled under the later construction action, so that the effectiveness of the reservoir is deteriorated; and the sound wave far detection imaging in the sound wave far detection imaging logging data beside the well of the dry layer does not have an obvious seam hole structure, and the dry layer is judged.
Based on any of the above embodiments, the number of clusters of the plurality of clusters is consistent with the number of levels of the preset plurality of validity levels.
Specifically, considering that all effectiveness levels are usually contained in the fracture-cavity reservoir to be evaluated, before the clustering operation in step 130 is performed, parameters of the clustering algorithm may be set based on the level numbers of the preset multiple effectiveness levels, specifically, the number of clusters in the clustering algorithm is directly set as the level number, the number of clusters obtained by clustering is consistent with the level number, and after the effectiveness grading is performed in step 140, the clusters and the effectiveness levels can be in one-to-one correspondence. For example, if the preset number of levels is 4, the number of clusters of the cluster is set to 4 before the clustering.
Based on any one of the embodiments, the method for grading the effectiveness of the fracture-cavity reservoir specifically comprises the following steps:
firstly, conventional logging, electrical imaging logging and sound wave remote detection are respectively carried out on a fracture-cavity reservoir body to be evaluated, and well periphery conventional logging data, well wall electrical imaging fracture-cavity logging interpretation data and well side sound wave remote detection imaging logging data are obtained.
Considering that the three types of data have different value intervals in a depth domain, the value interval of the well-side sound wave remote detection imaging logging data is used as a reference, well periphery conventional logging data with the same value interval as the value interval of the well-side sound wave remote detection imaging logging data is determined in a linear interpolation method, and well wall electrical imaging fracture-hole logging interpretation data with the same value interval as the value interval of the well-side sound wave remote detection imaging logging data is obtained in an equal-interval sampling mode.
After the unification of the value intervals is finished, the preset fusion proportion is used as a normalization constraint factor, and the well periphery conventional logging data, the well wall electric imaging seam hole logging interpretation data and the well side sound wave far detection imaging logging data are normalized respectively to obtain the normalized well periphery conventional logging data, the normalized well wall electric imaging seam hole logging interpretation data and the normalized well side sound wave far detection imaging logging data. Here, the preset fusion ratio is determined based on the number of attributes contained in the well periphery conventional logging data, the borehole wall electrical imaging fracture-hole logging interpretation data and the well side acoustic remote sensing imaging logging data. After normalization, the numerical value of the single attribute of the normalized well-periphery conventional logging data and the normalized borehole-wall electrical imaging fracture-hole logging interpretation data is between 0 and 0.04, and the two-dimensional data of the normalized well-side acoustic far detection imaging logging data is normalized between 0 and 0.4.
And then, sequentially superposing the normalized well periphery conventional well logging data and the normalized well wall electrical imaging fracture-hole well logging interpretation data on the normalized well side acoustic wave remote detection imaging well logging data according to data attributes to obtain multi-source fusion data.
On the basis, the multi-source fusion data is subjected to agglomeration hierarchical clustering from bottom to top to obtain 4 clustering clusters, and each clustering cluster corresponds to an effectiveness grade. Here, the number of cluster clusters coincides with the number of effectiveness ranks set in advance.
After the four clusters are obtained, the attribute characteristics of each cluster can be compared with the attribute characteristics of a plurality of preset effectiveness levels respectively to obtain the effectiveness levels corresponding to each cluster, and the effectiveness levels are used as effectiveness grading results of the fracture-cavern reservoir.
The method provided by the embodiment of the invention is based on a data superposition method with constraint normalization, realizes the deep fusion of multi-source data, and can control the fusion proportion of different source data by adjusting constraint factors; based on multi-source fusion data, reservoir clustering analysis and effectiveness evaluation can be realized by adopting a way of aggregation level clustering. Compared with the conventional effectiveness evaluation method, the method can be compatible with multi-source, multi-dimensional and multi-scale data volumes, and has good flexibility and usability.
Based on any one of the above embodiments, fig. 2 is a schematic flow chart of a fracture-cavity reservoir effectiveness grading method provided by another embodiment of the present invention, and as shown in fig. 2, after obtaining well-periphery conventional logging data, well-wall electrical imaging data, and well-side acoustic far-detection imaging logging data of a fracture-cavity reservoir to be evaluated, the well-periphery conventional logging data is preprocessed, where the preprocessing refers to normalizing the well-periphery conventional logging data to obtain normalized well-periphery conventional logging data; preprocessing the well-side sound wave remote detection imaging logging data, namely cutting and normalizing blank data of the well-side sound wave remote detection imaging logging data to obtain normalized well-side sound wave remote detection imaging logging data; processing the borehole wall electrical imaging data, specifically extracting the fracture-cavity information of the borehole wall electrical imaging data, extracting fracture-cavity characteristics on the basis, and normalizing after the characteristic extraction is completed to obtain normalized borehole wall electrical imaging fracture-cavity logging interpretation data.
And then, sequentially superposing the normalized well periphery conventional logging data and the normalized well wall electrical imaging fracture-hole logging interpretation data on the normalized well side acoustic wave remote detection imaging logging data according to data attributes to obtain multi-source fusion data.
And then, performing hierarchical clustering analysis and effectiveness evaluation on the multi-source fusion data to obtain an effectiveness grading result. Fig. 3 is a schematic diagram of an effectiveness grading result provided by an embodiment of the present invention, and as shown in fig. 3, clustering is performed on multi-source fusion data to obtain a three-source superposition data clustering analysis result shown in fig. 3, where 4 clustering clusters including a clustering cluster 1, a clustering cluster 2, a clustering cluster 3, and a clustering cluster 4 are specifically divided, and each stratum depth in a fracture-cavity reservoir body belongs to one of the 4 clustering clusters. And performing effectiveness grading based on the information, so as to obtain the corresponding relation between the clustering cluster and the effectiveness grade, thereby obtaining the corresponding relation between each stratum depth in the fracture-cavity reservoir body and the effectiveness grade.
Based on any one of the above embodiments, fig. 4 is a schematic structural diagram of a fracture-cavity reservoir effectiveness grading device provided by an embodiment of the present invention, as shown in fig. 4, the device includes:
the data determining unit 410 is used for determining the conventional logging data around the well, the well wall electrical imaging fracture-cavity logging interpretation data and the far detection imaging logging data of the sound waves beside the well of the fracture-cavity reservoir body to be evaluated;
the multi-source fusion unit 420 is used for performing multi-source fusion on the well periphery conventional logging data, the well wall electrical imaging fracture hole logging interpretation data and the well side acoustic far detection imaging logging data based on a preset fusion proportion to obtain multi-source fusion data;
a clustering unit 430, configured to cluster the depths of the stratums in the fracture-cavern reservoir based on the multi-source fusion data to obtain multiple cluster clusters;
and the grading unit 440 is used for calculating the conventional logging data and the average value of borehole wall electrical imaging fracture-cave interpretation data corresponding to each cluster based on each cluster, and determining the effectiveness grading result of the fracture-cave reservoir body by combining with the qualitative analysis of the well-side sound wave remote detection data.
According to the device provided by the embodiment of the invention, multisource fusion is carried out on the well periphery conventional well logging data, the well wall electrical imaging fracture hole well logging interpretation data and the well side sound wave remote detection imaging well logging data based on the preset fusion proportion, multisource fusion data are obtained, the multisource fusion data are clustered, the effectiveness grading result is obtained, the well periphery, the well wall and the well side multisource detection data are fused in the grading process, and the comprehensiveness of the data enables the effectiveness grading result to be more accurate.
Based on any of the embodiments described above, the multi-source fusion unit 420 is specifically configured to:
respectively normalizing the well periphery conventional logging data, the well wall electrical imaging fracture-hole logging interpretation data and the well side acoustic far detection imaging logging data by taking the preset fusion proportion as a normalization constraint factor to obtain normalized well periphery conventional logging data, normalized well wall electrical imaging fracture-hole logging interpretation data and normalized well side acoustic far detection imaging logging data;
and sequentially superposing the normalized well periphery conventional logging data and the normalized well wall electrical imaging fracture-cave logging interpretation data on the normalized well side acoustic remote detection imaging logging data according to data attributes to obtain the multi-source fusion data.
Based on any of the above embodiments, the apparatus further includes an interval adjusting unit, where the interval adjusting unit is configured to:
adjusting the value interval of the well periphery conventional logging data to the value interval of the well side sound wave remote detection imaging logging data based on a linear interpolation method;
and adjusting the value interval of the well wall electrical imaging fracture-cave well logging interpretation data to the value interval of the well side sound wave remote detection imaging well logging data based on an equal interval sampling method.
Based on any one of the above embodiments, the preset fusion proportion is determined based on the number of attributes respectively contained in the well periphery conventional logging data, the well wall electrical imaging fracture-hole logging interpretation data and the well side acoustic remote detection imaging logging data.
Based on any of the above embodiments, the classifying unit 440 is specifically configured to:
comparing the attribute characteristics of any cluster with the preset attribute characteristics of a plurality of validity levels to obtain the validity level corresponding to any cluster;
the attribute characteristics of any cluster are determined based on the attribute of each stratum depth in any cluster corresponding to at least one of the well periphery conventional logging data, the borehole wall electrical imaging fracture-hole logging interpretation data and the well side acoustic remote detection imaging logging data.
Based on any of the above embodiments, the preset multiple effectiveness levels include a class I reservoir, a class II reservoir, a class III reservoir, and a dry layer, where the class I reservoir and the class II reservoir are effective reservoirs, and the class III reservoir and the dry layer are ineffective reservoirs.
Based on any of the above embodiments, the number of clusters of the plurality of clusters is consistent with the preset number of levels of the plurality of validity levels.
Fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present invention, and as shown in fig. 5, the electronic device may include: a processor (processor)510, a communication Interface (Communications Interface)520, a memory (memory)530 and a communication bus 540, wherein the processor 510, the communication Interface 520 and the memory 530 communicate with each other via the communication bus 540. Processor 510 may call logical commands in memory 530 to perform the following method:
determining conventional logging data around a well, well wall electrical imaging fracture-cavity logging interpretation data and well-side acoustic remote detection imaging logging data of a fracture-cavity reservoir body to be evaluated;
performing multi-source fusion on the well periphery conventional well logging data, the well wall electrical imaging fracture hole well logging interpretation data and the well side acoustic far detection imaging well logging data based on a preset fusion proportion to obtain multi-source fusion data;
performing cluster analysis on each stratum depth in the fracture-cave reservoir body based on the multi-source fusion data to obtain a plurality of cluster clusters;
and calculating the average value of conventional logging data and borehole wall electrical imaging fracture-cave interpretation data corresponding to each cluster based on each cluster, and determining the effectiveness grading result of the fracture-cave reservoir body by combining with the qualitative analysis of the well-side sound wave remote detection data.
In addition, the logic commands in the memory 530 may be implemented in the form of software functional units and stored in a computer readable storage medium when the logic commands are sold or used as independent products. Based on such understanding, the technical solution of the present invention or a part thereof which substantially contributes to the prior art may be embodied in the form of a software product, which is stored in a storage medium and includes several commands for enabling a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk, and various media capable of storing program codes.
Embodiments of the present invention further provide a non-transitory computer-readable storage medium, on which a computer program is stored, where the computer program is implemented to perform the method provided in the foregoing embodiments when executed by a processor, and the method includes:
determining conventional logging data around a well, well wall electrical imaging fracture-cavity logging interpretation data and well-side acoustic remote detection imaging logging data of a fracture-cavity reservoir body to be evaluated;
performing multi-source fusion on the well periphery conventional logging data, the well wall electrical imaging fracture-hole logging interpretation data and the well side sound wave remote detection imaging logging data based on a preset fusion proportion to obtain multi-source fusion data;
clustering each stratum depth in the fracture-cave reservoir body based on the multi-source fusion data to obtain a plurality of clustering clusters;
and calculating the average value of conventional logging data and borehole wall electrical imaging fracture-cave interpretation data corresponding to each cluster based on each cluster, and determining the effectiveness grading result of the fracture-cave reservoir body by combining with the qualitative analysis of the well-side sound wave remote detection data.
The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium, such as ROM/RAM, magnetic disk, optical disk, etc., and includes commands for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method according to the embodiments or some parts of the embodiments.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A fracture-cavity reservoir effectiveness grading method combining multi-source information is characterized by comprising the following steps:
determining conventional logging data around a well, well wall electrical imaging fracture-cavity logging interpretation data and well-side acoustic remote detection imaging logging data of a fracture-cavity reservoir body to be evaluated;
performing multi-source fusion on the well periphery conventional well logging data, well wall electrical imaging fracture-cavity well logging interpretation data and well side acoustic far detection imaging well logging data based on a preset fusion proportion to obtain multi-source fusion data, wherein the multi-source fusion data comprises well logging data of each stratum depth in a fracture-cavity reservoir body;
clustering each stratum depth in the fracture-cave reservoir body based on the multi-source fusion data to obtain a plurality of clustering clusters; each cluster comprises a plurality of groups of logging data of stratum depths, the stratums corresponding to the same cluster belong to the same validity level, and the number of clusters is the same as the number of preset validity levels;
determining effectiveness grading results of the fracture-cavity reservoir based on the individual cluster clusters.
2. The method for grading effectiveness of a fracture-cavern reservoir in combination with multi-source information according to claim 1, wherein the multi-source fusion is performed on the well-periphery conventional logging data, the borehole-wall electrical imaging fracture-cavern logging interpretation data and the well-side acoustic far-detection imaging logging data based on a preset fusion proportion to obtain multi-source fusion data, and specifically comprises the following steps:
respectively normalizing the well periphery conventional logging data, the well wall electrical imaging fracture-hole logging interpretation data and the well side sound wave remote detection imaging logging data by taking the preset fusion proportion as a normalization constraint factor to obtain normalized well periphery conventional logging data, normalized well wall electrical imaging fracture-hole logging interpretation data and normalized well side sound wave remote detection imaging logging data;
and sequentially superposing the normalized well periphery conventional well logging data and the normalized well wall electrical imaging fracture-cave well logging interpretation data on the normalized well side acoustic remote detection imaging well logging data according to data attributes to obtain the multi-source fusion data.
3. The method for grading the effectiveness of a fracture-cavity reservoir in combination with multisource information according to claim 1, wherein the multisource fusion is performed on the well periphery conventional logging data, the well wall electrical imaging fracture-cavity logging interpretation data and the well side acoustic far detection imaging logging data based on a preset fusion proportion to obtain multisource fusion data, and the method further comprises the following steps:
adjusting the value interval of the well periphery conventional logging data to the value interval of the well side sound wave remote detection imaging logging data based on a linear interpolation method;
and adjusting the value interval of the well wall electrical imaging fracture-cave well logging interpretation data to the value interval of the well side sound wave remote detection imaging well logging data based on an equal interval sampling method.
4. The method for grading effectiveness of a fracture-cavern reservoir in combination with multi-source information as recited in claim 1, wherein said predetermined fusion ratio is determined based on the number of attributes respectively contained in said well-periphery conventional logging data, borehole wall electrical imaging fracture-cavern logging interpretation data and well-side acoustic remote sensing imaging logging data.
5. The joint multisource information fracture-cavity reservoir effectiveness grading method according to any one of claims 1 to 4, wherein the determining the effectiveness grading result of the fracture-cavity reservoir based on each cluster specifically comprises:
comparing the attribute characteristics of any cluster with the preset attribute characteristics of a plurality of validity levels to obtain the validity level corresponding to any cluster;
the attribute characteristics of any cluster are determined based on the attribute of each stratum depth in any cluster corresponding to at least one of the well periphery conventional logging data, the borehole wall electrical imaging fracture-hole logging interpretation data and the well side acoustic remote detection imaging logging data.
6. The method for grading the effectiveness of a fracture-cavity reservoir in combination with multisource information of claim 5, wherein the preset effectiveness levels comprise a class I reservoir, a class II reservoir, a class III reservoir and a dry layer, wherein the class I reservoir and the class II reservoir are effective reservoirs, and the class III reservoir and the dry layer are ineffective reservoirs.
7. A fracture-cavity reservoir effectiveness ranking method in conjunction with multisource information in accordance with claim 5 wherein the number of clusters of the plurality of clustered clusters is consistent with the number of levels of the preset plurality of effectiveness levels.
8. A fracture-cavern reservoir validity grading apparatus that combines multi-source information, comprising:
the data determining unit is used for determining the conventional logging data around the well, the well wall electrical imaging fracture-cavity logging interpretation data and the well-side acoustic remote detection imaging logging data of the fracture-cavity reservoir body to be evaluated;
the multi-source fusion unit is used for carrying out multi-source fusion on the well periphery conventional well logging data, the well wall electrical imaging fracture-cave well logging interpretation data and the well side sound wave far detection imaging well logging data based on a preset fusion proportion to obtain multi-source fusion data, and the multi-source fusion data comprises well logging data of all stratum depths in the fracture-cave reservoir body;
the clustering unit is used for clustering the depths of all stratums in the fracture-cavern reservoir body based on the multi-source fusion data to obtain a plurality of clustering clusters; each cluster comprises a plurality of groups of logging data of stratum depths, the stratums corresponding to the same cluster belong to the same validity level, and the number of clusters is the same as the number of preset validity levels;
and the grading unit is used for determining an effectiveness grading result of the fracture-cave reservoir body based on each cluster.
9. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor when executing the program implements the steps of the method for fracture-hole-reservoir-validity ranking in conjunction with multi-source information of any one of claims 1 to 7.
10. A non-transitory computer readable storage medium having stored thereon a computer program, which when executed by a processor, performs the steps of the joint multisource information fracture-cavity reservoir validity ranking method of any of claims 1 to 7.
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