An intelligent prediction method for fractures in tight carbonate reservoir has been established by upgrading single-well fracture identification and interwell fracture trend prediction with artificial intelligence,mo...An intelligent prediction method for fractures in tight carbonate reservoir has been established by upgrading single-well fracture identification and interwell fracture trend prediction with artificial intelligence,modifying construction of interwell fracture density model,and modeling fracture network and making fracture property equivalence.This method deeply mines fracture information in multi-source isomerous data of different scales to reduce uncertainties of fracture prediction.Based on conventional fracture indicating parameter method,a prediction method of single-well fractures has been worked out by using 3 kinds of artificial intelligence methods to improve fracture identification accuracy from 3 aspects,small sample classification,multi-scale nonlinear feature extraction,and decreasing variance of the prediction model.Fracture prediction by artificial intelligence using seismic attributes provides many details of inter-well fractures.It is combined with fault-related fracture information predicted by numerical simulation of reservoir geomechanics to improve inter-well fracture trend prediction.An interwell fracture density model for fracture network modeling is built by coupling single-well fracture identification and interwell fracture trend through co-sequential simulation.By taking the tight carbonate reservoir of Oligocene-Miocene AS Formation of A Oilfield in Zagros Basin of the Middle East as an example,the proposed prediction method was applied and verified.The single-well fracture identification improves over 15%compared with the conventional fracture indication parameter method in accuracy rate,and the inter-well fracture prediction improves over 25%compared with the composite seismic attribute prediction.The established fracture network model is well consistent with the fluid production index.展开更多
Based on a large number of field outcrops and cores taken systematically from boreholes,by microscopic observa-tion,physical property analysis,mineralogy analysis,geochemical analysis etc.,reservoir characteristics of...Based on a large number of field outcrops and cores taken systematically from boreholes,by microscopic observa-tion,physical property analysis,mineralogy analysis,geochemical analysis etc.,reservoir characteristics of the first member of Middle Permian Maokou Formation in Sichuan Basin("Mao 1 Me mber"for short)are analyzed.(1)Rhythmic limestone-marl reservoirs of this member mostly exist in marl layers are a set of tight carbonate fracture-pore type reservoir with low porosity and low permeability,with multiple types of storage space,mainly secondary dissolution pores and fissures of clay minerals.(2)The clay minerals are mainly diagenetic clay minerals,such as sepiolite,talc and their intermediate products,aliettite,with hardly terrigenous clay minerals,and the reservoir in different regions have significant differences in the types of clay minerals.(3)The formation of high quality tight carbonate reservoir with limestone-marl interbeds is related to the differential diagene-sis in the early seawater burial stage and the exposure karstification in the early diagenetic stage.It is inferred through th e study that the inner ramp of southwestern Sichuan Basin is more likely to have sweet spots with high production,while the outer ramp in eastern Sichuan Basin is more likely to have large scale contiguous reservoir with low production.展开更多
Identifying fractures along a well trajectory is of immense significance in determining the subsurface fracture network distribution.Typically,conventional logs exhibit responses in fracture zones,and almost all wells...Identifying fractures along a well trajectory is of immense significance in determining the subsurface fracture network distribution.Typically,conventional logs exhibit responses in fracture zones,and almost all wells have such logs.However,detecting fractures through logging responses can be challenging since the log response intensity is weak and complex.To address this problem,we propose a deep learning model for fracture identification using deep forest,which is based on a cascade structure comprising multi-layer random forests.Deep forest can extract complex nonlinear features of fractures in conventional logs through ensemble learning and deep learning.The proposed approach is tested using a dataset from the Oligocene to Miocene tight carbonate reservoirs in D oilfield,Zagros Basin,Middle East,and eight logs are selected to construct the fracture identification model based on sensitivity analysis of logging curves against fractures.The log package includes the gamma-ray,caliper,density,compensated neutron,acoustic transit time,and shallow,deep,and flushed zone resistivity logs.Experiments have shown that the deep forest obtains high recall and accuracy(>92%).In a blind well test,results from the deep forest learning model have a good correlation with fracture observation from cores.Compared to the random forest method,a widely used ensemble learning method,the proposed deep forest model improves accuracy by approximately 4.6%.展开更多
基金Supported by the China Youth Program of National Natural Science Foundation(42002134)The 14th Special Support Program of China Postdoctoral Science Foundation(2021T140735).
文摘An intelligent prediction method for fractures in tight carbonate reservoir has been established by upgrading single-well fracture identification and interwell fracture trend prediction with artificial intelligence,modifying construction of interwell fracture density model,and modeling fracture network and making fracture property equivalence.This method deeply mines fracture information in multi-source isomerous data of different scales to reduce uncertainties of fracture prediction.Based on conventional fracture indicating parameter method,a prediction method of single-well fractures has been worked out by using 3 kinds of artificial intelligence methods to improve fracture identification accuracy from 3 aspects,small sample classification,multi-scale nonlinear feature extraction,and decreasing variance of the prediction model.Fracture prediction by artificial intelligence using seismic attributes provides many details of inter-well fractures.It is combined with fault-related fracture information predicted by numerical simulation of reservoir geomechanics to improve inter-well fracture trend prediction.An interwell fracture density model for fracture network modeling is built by coupling single-well fracture identification and interwell fracture trend through co-sequential simulation.By taking the tight carbonate reservoir of Oligocene-Miocene AS Formation of A Oilfield in Zagros Basin of the Middle East as an example,the proposed prediction method was applied and verified.The single-well fracture identification improves over 15%compared with the conventional fracture indication parameter method in accuracy rate,and the inter-well fracture prediction improves over 25%compared with the composite seismic attribute prediction.The established fracture network model is well consistent with the fluid production index.
基金Supported by the Scientific and Technological Research Projects of Sinopec(P20059-3)Scientific and Technological Research Projects of Southwest Branch Company(KJ-633-2103).
文摘Based on a large number of field outcrops and cores taken systematically from boreholes,by microscopic observa-tion,physical property analysis,mineralogy analysis,geochemical analysis etc.,reservoir characteristics of the first member of Middle Permian Maokou Formation in Sichuan Basin("Mao 1 Me mber"for short)are analyzed.(1)Rhythmic limestone-marl reservoirs of this member mostly exist in marl layers are a set of tight carbonate fracture-pore type reservoir with low porosity and low permeability,with multiple types of storage space,mainly secondary dissolution pores and fissures of clay minerals.(2)The clay minerals are mainly diagenetic clay minerals,such as sepiolite,talc and their intermediate products,aliettite,with hardly terrigenous clay minerals,and the reservoir in different regions have significant differences in the types of clay minerals.(3)The formation of high quality tight carbonate reservoir with limestone-marl interbeds is related to the differential diagene-sis in the early seawater burial stage and the exposure karstification in the early diagenetic stage.It is inferred through th e study that the inner ramp of southwestern Sichuan Basin is more likely to have sweet spots with high production,while the outer ramp in eastern Sichuan Basin is more likely to have large scale contiguous reservoir with low production.
基金funded by the National Natural Science Foundation of China(Grant No.42002134)China Postdoctoral Science Foundation(Grant No.2021T140735).
文摘Identifying fractures along a well trajectory is of immense significance in determining the subsurface fracture network distribution.Typically,conventional logs exhibit responses in fracture zones,and almost all wells have such logs.However,detecting fractures through logging responses can be challenging since the log response intensity is weak and complex.To address this problem,we propose a deep learning model for fracture identification using deep forest,which is based on a cascade structure comprising multi-layer random forests.Deep forest can extract complex nonlinear features of fractures in conventional logs through ensemble learning and deep learning.The proposed approach is tested using a dataset from the Oligocene to Miocene tight carbonate reservoirs in D oilfield,Zagros Basin,Middle East,and eight logs are selected to construct the fracture identification model based on sensitivity analysis of logging curves against fractures.The log package includes the gamma-ray,caliper,density,compensated neutron,acoustic transit time,and shallow,deep,and flushed zone resistivity logs.Experiments have shown that the deep forest obtains high recall and accuracy(>92%).In a blind well test,results from the deep forest learning model have a good correlation with fracture observation from cores.Compared to the random forest method,a widely used ensemble learning method,the proposed deep forest model improves accuracy by approximately 4.6%.
