A method for efficiently correlating fluvial facies sand bodies of different categories

Abstract

The application provides a classification-adapted high-efficiency comparison method for fluvial facies sand bodies, which mainly comprises the following steps: step 1, fluvial facies sand body type subdivision based on well-seismic combined fine dissection; step 2, determining sand body fine comparison evaluation parameters; comparing well spacing density with the fine comparison evaluation parameters of the sand body to obtain accurate research results; establishing a high-precision prototype model based on the research results of the well spacing density; and guiding the comparison work of other strata based on the prototype model. The fluvial facies sand body comparison method is aimed at the problems of many wells, large stratum comparison workload and low efficiency in the dense well network work area of the eastern whole set old oilfield, optimizes the target block, realizes sand body fine dissection by using well-seismic combination, establishes a high-precision prototype model by analyzing the relationship between well spacing density and comparison accuracy, and further provides guidance for fine stratum comparison of other same type fluvial facies sand bodies, thereby improving the accuracy and efficiency of fluvial facies sand body comparison.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of oil reservoir geology research and development, and particularly relates to a classified and adaptive high-efficiency comparison method for fluvial facies sand bodies. BACKGROUND

[0002] At present, the main blocks of Shengli Oilfield have successively entered the development stage of "three highs", and nearly one-third of the high-oil-saturation remaining oil is stored in enrichment areas controlled by small-scale geological bodies such as small faults and thin interbeds, and the development objects are mainly of the type of lithology-structure double control reservoirs, which are mainly characterized by difficult plane combination, vertically thin reservoirs, fast horizontal change, and difficult description, and nearly 40% of the failed wells are caused by inaccurate reservoir prediction, so improving the reservoir prediction accuracy is a key factor to improve the drilling success rate. Fine stratigraphic correlation is of great significance to the implementation of reservoir connectivity and the determination of sand body distribution rule, and can effectively improve the accuracy and accuracy of reservoir description. However, the conventional sand body comparison method is to compare the reservoirs of all wells in the work area according to the known data, which is time-consuming and laborious. SUMMARY

[0003] The present application aims to at least solve one of the technical problems in the prior art, and provides a classified and adaptive high-efficiency comparison method for fluvial facies sand bodies, which can improve the reservoir comparison accuracy and efficiency in dense well pattern areas and guide the deployment of development well pattern.

[0004] According to a first aspect of the present application, a classified and adaptive high-efficiency comparison method for fluvial facies sand bodies is provided, which specifically comprises the following steps:

[0005] Subdivide the types of fluvial facies sand bodies based on well-seismic joint fine dissection;

[0006] Determine the fine comparison evaluation parameters of the sand bodies;

[0007] Compare the well pattern density with the comparison evaluation parameters of the sand bodies to obtain accurate research results;

[0008] Establish a high-precision prototype model based on the research results of the well pattern density;

[0009] Guide the stratigraphic correlation of the same type of fluvial facies sand bodies based on the prototype model.

[0010] According to the first aspect of the present application, the subdivision of the types of fluvial facies sand bodies based on well-seismic joint fine dissection specifically comprises: determining the seismic reflection characteristics of different lithology, barrier thickness and sand body superposition through seismic forward modeling, guiding the identification of the river channel, and combining the genesis, morphology and small layer plane of the channel sand body to classify the sand bodies in the entire work area.

[0011] According to the first aspect of the present application, the fine correlation evaluation parameters of the sand body are determined, wherein the correlation evaluation parameters include the number of sand body development and the comprehensive control index of the sand body, and the comprehensive control index of the sand body is defined as follows:

[0012]

[0013]

[0014] Comprehensive control index = (control rate + accuracy rate) / 2

[0015] According to the first aspect of the present application, the well pattern density and the sand body correlation accuracy research specifically includes: through the sparse well pattern, the fine correlation evaluation parameters of each type of sand body under different well pattern densities are compared, the well pattern density required when reaching the accuracy standard is counted, and the influence of the well pattern density on the correlation accuracy of different types of sand bodies is analyzed.

[0016] According to the first aspect of the present application, the research results of the well pattern density establish a high-precision prototype model, and specifically include the following steps:

[0017] Step 1: Make full use of the advantages of rich well data in the research area, based on the geological research results obtained in the above steps, and apply the deterministic reservoir modeling technology constrained by geological understanding to establish an initial prototype model;

[0018] Step 2: Apply multi-point geostatistical reservoir modeling technology to analyze and research each sensitive parameter in the initial prototype model one by one, and finally obtain a high-precision prototype model.

[0019] According to the first aspect of the present application, the sensitive parameters include at least one of the training image, the sand-shale ratio, the reference ratio and the probability body.

[0020] According to the first aspect of the present application, the stratigraphic correlation work of the same type of fluvial facies sand body based on the prototype model specifically includes: using the prototype model to guide the stratigraphic correlation of other same type of reservoirs, effectively improving the correlation accuracy and efficiency, and obtaining accurate sand body correlation results of adaptive well pattern.

[0021] The embodiments of the present application have at least the following technical effects:

[0022] Reservoir correlation in densely networked well areas is labor-intensive, highly repetitive, and inefficient. When the well density reaches a certain value, the influence of well spacing on the reservoir correlation results becomes minimal. This invention uses the well density at which the reservoir correlation results achieve the required accuracy as the correlation outcrop. Combined with a well-seismic joint fine identification method, it studies the degree of control of different types of channel sand bodies under different well spacing conditions in densely networked well areas and the influence of prototype models on reservoir prediction accuracy under different well spacings. This yields the optimal well density applicable to different types of sand bodies, and a high-precision prototype model is established using multi-point geostatistics. This provides guidance for stratigraphic correlation work on the same type of fluvial facies sand bodies. This method is of great significance for improving the accuracy and efficiency of reservoir correlation in densely networked well areas and guiding the deployment of development well networks. Attached Figure Description

[0023] The present invention will be further described below with reference to the accompanying drawings and embodiments;

[0024] Figure 1 This is a flowchart of an efficient comparative method for classifying and adapting fluvial sand bodies according to an embodiment of the present invention.

[0025] Figure 2 This is a schematic diagram illustrating sand body classification based on seismic reflection characteristics in an embodiment of the present invention;

[0026] Figure 3 This is a schematic diagram illustrating the subdivision of different types of sand bodies within the work area in an embodiment of the present invention;

[0027] Figure 4 This is a schematic diagram of sand body distribution at a well spacing of 70m in an embodiment of the present invention;

[0028] Figure 5 This is a comparison diagram of sand body distribution under different dilution well spacing in the embodiments of the present invention;

[0029] Figure 6 This is a schematic cross-sectional view of the prototype model at a well spacing of 70m in an embodiment of the present invention;

[0030] Figure 7 This is a comparison diagram of the prototype model cross-section under different dilution well spacing in the embodiments of the present invention. Detailed Implementation

[0031] This section will describe in detail specific embodiments of the present invention. Preferred embodiments of the present invention are shown in the accompanying drawings. The purpose of the drawings is to supplement the textual description with graphics, so that people can intuitively and vividly understand each technical feature and overall technical solution of the present invention, but they should not be construed as limiting the scope of protection of the present invention.

[0032] like Figure 1 The diagram shows a flowchart of an efficient correlation method for fluvial facies sand bodies based on classification and adaptation according to the present invention, which includes the following steps:

[0033] Step 110, based on the river facies sand body type subdivision of well-seismic joint fine dissection.

[0034] Specifically, in the embodiment of the present application, the seismic reflection characteristics of different lithology, barrier thickness and sand body superimposition are determined through seismic forward modeling, guiding the channel identification, and combining the genesis, morphology and small layer plan of the channel sand body, the sand body in the whole work area is classified.

[0035] In the embodiment of the present application, the seismic reflection characteristics of different lithology, barrier thickness and sand body superimposition are analyzed through seismic forward modeling, and after the over well profile of the drilled well is combined, we find that: the top of the positive polarity profile thick block sandstone corresponds to the seismic wave valley, the complex wave corresponds to the sand and mud interbedding, such as Figure 2 As shown in the figure, it is a sand body classification diagram according to the seismic reflection characteristics in the embodiment of the present application.

[0036] According to the above characteristics, in one specific embodiment of the present application, the sand body types of each small layer in the work area are subdivided in combination with the sedimentary conditions and sand body morphology of the research area, and are divided into four types of sand bodies: multi-stage superimposed sheet sand body, migration superimposed strip sand body, strip channel sand body and potato-shaped sand body, as shown in Figure 3 As shown in the figure, it is a sand body subdivision diagram of different types in the work area in the embodiment of the present application.

[0037] Step 120, determining the sand body fine correlation evaluation parameter.

[0038] In the embodiment of the present application, the correlation evaluation parameter can accurately and comprehensively reflect the sand body correlation result, the evaluation criterion is clear, and the evaluation standard is reliable.

[0039] As a specific example of the embodiment of the present application, two evaluation parameters are determined: the number of sand body development and the sand body comprehensive control index, wherein the sand body comprehensive control index is defined as follows:

[0040]

[0041]

[0042] Comprehensive control index = (control rate + accuracy rate) / 2

[0043] Determine the sand body fine correlation evaluation parameter: the number of sand body development and the sand body comprehensive control index, in the embodiment of the present application, the well spacing density is thinned, and the control degree of well spacing density on different types of sand bodies is studied as the target, and the sand body development under five well spacings of 120m, 180m, 240m and 350m is analyzed. As shown in Figure 4 , Figure 5As shown in the table, when the sand body comprehensive control index is more than 80%, the well pattern density of the potato-shaped sand body is less than 1.5, and the well pattern density of the strip-shaped channel sand body is less than 2.5; and the well pattern density has little effect on the migration superimposed strip-shaped sand body and the multi-period superimposed sheet-shaped sand body.

[0044] In step 130, the well pattern density is compared with the contrast evaluation parameter of the sand body, and accurate research results are obtained.

[0045] Specifically, in the embodiment of the present application, the fine contrast evaluation parameters of different types of sand bodies under different well pattern densities are compared, the well pattern density required to reach the accuracy standard is counted, and the influence of the well pattern density on the contrast accuracy of different types of sand bodies is analyzed.

[0046] It is found through analysis that, as the well pattern density decreases, the control degree on the channel sand body becomes weaker and weaker; the well pattern required to control different types of channel sand bodies is quite different; the smaller the distribution range of the sand body is, the higher the dispersion degree is, and the larger the well pattern density required to control the sand body is. It is finally determined that the best well pattern density of the multi-period superimposed sheet-shaped sand body and the migration superimposed strip-shaped sand body is 350 m; the best well pattern density of the strip-shaped channel sand body is 120 m; and the best well pattern density of the potato-shaped sand body is less than 100 m.

[0047] In step 140, a high-precision prototype model is established based on the research results of the dense well pattern. The following steps are taken:

[0048] In step 1, the initial prototype model under the condition of the dense well pattern is established by fully utilizing the advantage of rich well data in the research area, based on the geological research results obtained in the above steps, and by applying the deterministic reservoir modeling technology constrained by geological understanding.

[0049] In step 2, the high-precision prototype model is finally obtained by analyzing and researching each sensitive parameter in the initial prototype model one by one by applying the multiple-point geostatistics reservoir modeling technology.

[0050] As shown in the table, the high-precision prototype model is finally established by analyzing and researching each sensitive parameter in the initial prototype model one by one by applying the multiple-point geostatistics reservoir modeling technology. Figure 6 As shown in the table, the high-precision prototype model is finally established by analyzing and researching each sensitive parameter in the initial prototype model one by one by applying the multiple-point geostatistics reservoir modeling technology. Figure 7

[0051] In step 150, the stratigraphic correlation work of the same type of fluvial facies sand body is guided based on the prototype model. The high-precision prototype model is used to guide the stratigraphic correlation of other same type of reservoirs, so as to effectively improve the correlation accuracy and efficiency, and obtain the accurate sand body correlation results of the adaptive well pattern. ​

[0052] The sand body correlation method is applied to effectively improve the reservoir correlation precision and efficiency, and is important for guiding reservoir research and development well pattern deployment of different types of channel sand bodies.

[0053] The well pattern density and sand body precision relationship obtained in the above steps and the high-precision prototype model are applied in the same type of reservoir, and the fine stratum correlation in the research area is realized through classification research and optimization adaptation, which improves the stratum correlation precision, avoids the repetition of workload, effectively improves the correlation efficiency, and also provides guidance for related reservoir research and development well pattern deployment.

[0054] The embodiments of the present application are described in detail above in combination with the drawings, but the present application is not limited to the above embodiments, and various changes can be made within the knowledge of those skilled in the art without departing from the purpose of the present application.

Claims

1. A method for efficiently correlating class adapted fluvial sand bodies, characterized in that, Specifically comprising the following steps: Based on the river facies sand body type of the well-seismic combined fine dissection, the sand body is subdivided; The sand body fine correlation evaluation parameter is determined; the well pattern density is compared with the fine correlation evaluation parameter of the sand body, and the accuracy research result is obtained; the high-precision prototype model is established based on the research result of the well pattern density; Based on the prototype model, the correlation work of other strata is guided; The river facies sand body type subdivision based on the well-seismic combined fine dissection specifically comprises: through seismic forward simulation, the seismic reflection characteristics of different lithology, interlayer thickness and sand body superposition are determined, the channel identification is guided, and the sand body in the whole work area is classified in combination with the genesis, morphology and small layer plan of the channel sand body; The sand body fine correlation evaluation parameter is determined, wherein the correlation evaluation parameter comprises the number of sand body development and the sand body comprehensive control index, wherein the sand body comprehensive control index is defined as follows: ； Comprehensive control index = (control rate + accuracy rate) / 2; the high-precision prototype model is established based on the research result of the well pattern density, specifically comprising the following steps: Step 1: fully utilizing the advantage of rich well data in the research area, based on the obtained geological research result, the initial prototype model is established by applying the deterministic reservoir modeling technology constrained by geological understanding; 2. The method of claim 1, wherein, Step 2: applying the multi-point geostatistics reservoir modeling technology, each sensitive parameter in the initial prototype model is analyzed and researched one by one, and finally the high-precision prototype model is obtained; the sensitive parameter comprises at least one of training image, sand-shale ratio, reference ratio and probability body.

3. The method of claim 1, wherein, The well pattern density is compared with the fine correlation evaluation parameter of the sand body, and the accuracy research result is obtained, specifically comprising: by thinning the well pattern, the fine correlation evaluation parameters of each type of sand body under different well pattern densities are compared, the well pattern density required to reach the accuracy standard is counted, and the influence of well pattern density on the correlation accuracy of different types of sand body is analyzed. Based on the prototype model, the correlation work of other strata is guided, and the accurate sand body correlation result of the adaptive well pattern is obtained.

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