[특허]Predicting sand-grain composition and sand texture

ExxonMobil Upstream Research Company

검색어에 아래의 연산자를 사용하시면 더 정확한 검색결과를 얻을 수 있습니다.
검색연산자

검색도움말
검색연산자	기능	검색시 예
()	우선순위가 가장 높은 연산자	예1) (나노 (기계 \| machine))
공백	두 개의 검색어(식)을 모두 포함하고 있는 문서 검색	예1) (나노 기계) 예2) 나노 장영실
\|	두 개의 검색어(식) 중 하나 이상 포함하고 있는 문서 검색	예1) (줄기세포 \| 면역) 예2) 줄기세포 \| 장영실
!	NOT 이후에 있는 검색어가 포함된 문서는 제외	예1) (황금 !백금) 예2) !image
*	검색어의 *란에 0개 이상의 임의의 문자가 포함된 문서 검색	예) semi*
""	따옴표 내의 구문과 완전히 일치하는 문서만 검색	예) "Transform and Quantization"

과학기술 지식인프라 ScienceON

연합인증

연합인증 가입 기관의 연구자들은 소속기관의 인증정보(ID와 암호)를 이용해 다른 대학, 연구기관, 서비스 공급자의 다양한 온라인 자원과 연구 데이터를 이용할 수 있습니다.

이는 여행자가 자국에서 발행 받은 여권으로 세계 각국을 자유롭게 여행할 수 있는 것과 같습니다.

연합인증으로 이용이 가능한 서비스는 NTIS, DataON, Edison, Kafe, Webinar 등이 있습니다.

한번의 인증절차만으로 연합인증 가입 서비스에 추가 로그인 없이 이용이 가능합니다.

다만, 연합인증을 위해서는 최초 1회만 인증 절차가 필요합니다. (회원이 아닐 경우 회원 가입이 필요합니다.)

연합인증 절차는 다음과 같습니다.

최초이용시에는
ScienceON에 로그인 → 연합인증 서비스 접속 → 로그인 (본인 확인 또는 회원가입) → 서비스 이용

그 이후에는
ScienceON 로그인 → 연합인증 서비스 접속 → 서비스 이용

연합인증을 활용하시면 KISTI가 제공하는 다양한 서비스를 편리하게 이용하실 수 있습니다.

특허 상세정보

MyON담기

내보내기 페이스북 공유 트위터 공유 카카오톡 공유

Predicting sand-grain composition and sand texture

특허상세정보
국가/구분	United States(US) Patent 등록
국제특허분류(IPC7판)	G06F-017/00 G06N-005/00
미국특허분류(USC)	706/045
출원번호	UP-0631740 (2005-05-31)
등록번호	US-7747552 (2010-07-19)
국제출원번호	PCT/US2005/018821 (2005-05-31)
§371/§102 date	20070105 (20070105)
국제공개번호	WO06/016942 (2006-02-16)
발명자 / 주소	Kairo, Suzanne Heins, William A. Love, Karen M.
출원인 / 주소	ExxonMobil Upstream Research Company
대리인 / 주소	ExxonMobil Upstream Research Company - Law Dept.
인용정보	피인용 횟수 : 1 인용 특허 : 56

초록
▼

A method and apparatus for predicting sand-grain composition and sand texture are disclosed. A first set of system variables associated with sand-grain composition and sand texture is selected (605). A second set of system variables directly or indirectly causally related to the first set of variables is also selected (610). Data for each variable in the second set is estimated or obtained (615). A network with nodes including both sets of variables is formed (625). The network has a directional links connecting interdependent nodes. The directional link...

대표
청구항
▼

What is claimed is: 1. A method for predicting sand-grain composition and sand texture comprising: selecting a first set of system variables, said first set associated with sand-grain composition and sand texture; selecting a second set of system variables, said second set being directly or indirectly causally related to said first set of variables; obtaining or estimating data for each variable in the second set; forming a network with nodes comprising both sets of variables, having directional links connecting interdependent nodes, said directional links honoring known causality relationships; and using a Bayesian Network algorithm with said data to solve the network for said first set of variables and their associated uncertainties. 2. The method of claim 1 further comprising: appraising the quality of selected data; and including the quality appraisals in the network and in the application of the Bayesian Network algorithm. 3. The method of claim 1, where the system has a behavior, the method further comprising: selecting the first set of variables and the second set of variables so that together they are sufficiently complete to account for the behavior of the system. 4. The method of claim 1, where forming the network comprises: forming a third set of intermediate nodes interposed between at least some of the nodes representing the first set of system variables and at least some of the nodes representing the second set of system variables. 5. The method of claim 1, where selecting the first set of system variables comprises: selecting one or more system variables associated with sand-grain composition; and selecting one or more system variables associated with sand texture. 6. The method of claim 1 where selecting the second set of system variables comprises: selecting one or more system variables associated with hinterland geology; selecting one or more system variables associated with hinterland weathering and transport; and selecting one or more system variables associated with basin transport and deposition. 7. A method for predicting sand-grain composition and sand texture comprising: establishing one or more root nodes in a Bayesian network; establishing one or more leaf nodes in the Bayesian network; coupling the root nodes to the leaf nodes to enable the Bayesian network to predict sand-grain composition and texture. 8. The method of claim 7 where establishing the one or more root nodes comprises: establishing one or more root nodes for hinterland geology; establishing one or more root nodes for hinterland weathering and transport; and establishing one or more root nodes for basin transport and deposition. 9. The method of claim 8 where establishing one or more root nodes for hinterland geology comprises: establishing a root node for tectonic setting. 10. The method of claim 8 where establishing one or more root nodes for hinterland weathering and transport comprises: establishing a root node for climate; establishing a root node for rate of hinterland uplift; and establishing a root node for hinterland transport distance. 11. The method of claim 8 where establishing one or more root nodes for basin transport and deposition comprises: establishing a root node for rate of basin subsidence; establishing a root node for basin fluvial transport distance; and establishing a root node for depositional facies. 12. The method of claim 7 where establishing one or more leaf nodes comprises: establishing one or more leaf nodes for sand-grain composition; and establishing one or more leaf nodes for sand texture. 13. The method of claim 12 where establishing one or more leaf nodes for sand texture comprises: establishing a leaf node for grain size; establishing a leaf node for degree of sorting; and establishing a leaf node for deposited matrix abundance. 14. The method of claim 12 where establishing the leaf node for grain composition comprises: establishing a leaf node for final CIBU sand; establishing a leaf node for final CISU sand; establishing a leaf node for final CAMBU sand; establishing a leaf node for final CAMSU sand; establishing a leaf node for final SAMV sand; and establishing a leaf node for final SAMP sand. 15. The method of claim 7 further comprising: establishing one or more intermediate nodes; and where coupling the root nodes to the leaf nodes to enable the Bayesian network to predict sand-grain composition and texture comprises: coupling at least some of the one or more root nodes to at least some of the one or more leaf nodes through the one or more intermediate nodes. 16. The method of claim 15 where coupling the root nodes to the leaf nodes to enable the Bayesian network to predict sand-grain composition and texture comprises: coupling the root nodes to the leaf nodes in causal relationships that honor observations of natural systems. 17. The method of claim 15 where coupling the root nodes to the leaf nodes to enable the Bayesian network to predict sand-grain composition and texture comprises: defining for each root node one or more outputs that connect to other nodes that the root node causes; defining for each intermediate node: one or more inputs that connect to the other nodes that cause the intermediate node; one or more outputs that connect to other nodes that the intermediate node causes; and defining for each leaf node one or more inputs that connect to other nodes that cause the leaf node. 18. The method of claim 15 where establishing the one or more root nodes comprises: creating a probability table for each root node; each probability table having one or more predefined states; and each predefined state having associated with it a probability that the root node is in that state. 19. The method of claim 18 where creating the probability table for each root node comprises: completing the probability table based on quantitative observations of a natural system associated with the root node. 20. The method of claim 19 further comprising: modifying the probability table based on quantitative observations of the natural system associated with the root node. 21. The method of claim 15 where establishing the one or more leaf nodes comprises: creating a probability table for each leaf node; each probability table having a respective one or more predefined states; and each predefined state having associated with it a probability that the leaf node is in that state. 22. The method of claim 15 where each leaf node has a predefined number of inputs and where creating the probability table for each leaf node comprises: creating a probability table having the respective predefined number of input dimensions. 23. The method of claim 22 where creating the probability table for each leaf node comprises: completing the probability table with data reflecting quantitative observations of a natural system associated with the leaf node. 24. The method of claim 23 further comprising: modifying the probability table based on quantitative observations of the natural system associated with the leaf node. 25. The method of claim 15 where establishing the one or more intermediate nodes comprises: creating a probability table for each intermediate node; each probability table having a respective one or more predefined states; and each predefined state having associated with it a probability that the intermediate node is in that state. 26. The method of claim 15 where each intermediate node has a predefined number of inputs and where creating the probability table for each intermediate node comprises: creating a probability table having the respective predefined number of input dimensions. 27. The method of claim 26 where creating the probability table for each intermediate node comprises: completing the probability table with data reflecting quantitative observations of a natural system associated with the intermediate node. 28. The method of claim 27 further comprising: modifying the probability table based on quantitative observations of the natural system associated with the intermediate node. 29. A Bayesian network comprising: one or more root nodes; one or more leaf nodes; the root nodes being coupled to the leaf nodes to enable the Bayesian network to predict sand-grain composition and texture. 30. The Bayesian network of claim 29 the one or more root nodes comprise: one or more root nodes for hinterland geology; one or more root nodes for hinterland weathering and transport; and one or more root nodes for basin transport and deposition. 31. The Bayesian network of claim 30 where the one or more root nodes for hinterland geology comprise: a root node for tectonic setting; and a root node for dominant geologic units. 32. The Bayesian network of claim 30 where the one or more root nodes for hinterland weathering and transport comprise: a root node for climate; a root node for rate of hinterland uplift; and a root node for hinterland transport distance. 33. The Bayesian network of claim 30 where the one or more root nodes for basin transport and deposition comprise: a root node for rate of basis subsidence; a root node for basin fluvial transport distance; and a root node for depositional facies. 34. The Bayesian network of claim 29 where the one or more root nodes comprise: one or more leaf nodes for sand-grain composition; and one or more leaf nodes for sand texture. 35. The Bayesian network of claim 34 where the one or more root nodes for sand texture comprise: a leaf node for grain size; a leaf node for degree of sorting; and a leaf node for deposited matrix abundance. 36. The Bayesian network of claim 35 where the leaf node for grain size comprises: a leaf node for final CIBU sand; a leaf node for final CISU sand; a leaf node for final CAMBU sand; a leaf node for final CAMSU sand; a leaf node for final SAMV sand; and a leaf node for final SAMP sand. 37. The Bayesian network of claim 29 further comprising: one or more intermediate nodes; and where the coupling between the root nodes and the leaf nodes to enable the Bayesian network to predict sand-grain composition and texture comprises: at least some of the one or more root nodes be coupled to at least some of the one or more leaf nodes through the one or more intermediate nodes. 38. The Bayesian network of claim 37 where the coupling between the root nodes and the leaf nodes to enable the Bayesian network to predict sand-grain composition and texture comprises: the root nodes being coupled to the leaf nodes in causal relationships that honor observations of natural systems. 39. The Bayesian network of claim 37 where the coupling between the root nodes and the leaf nodes to enable the Bayesian network to predict sand-grain composition and texture comprises: for each root node, one or more outputs that connect to other nodes that the root node causes; for each intermediate node: one or more inputs that connect to the other nodes that cause the intermediate node; one or more outputs that connect to other nodes that the intermediate node causes; and for each leaf node one or more inputs that connect to other nodes that cause the leaf node. 40. The Bayesian network of claim 37 where the one or more root nodes comprises: a probability table for each root node; each probability table having one or more predefined states; and each predefined state having associated with it a probability that the root node is in that state. 41. The Bayesian network of claim 40 where the probability table for each root node comprises: data reflecting quantitative observations of a natural system associated with the root node. 42. The Bayesian network of claim 41 further comprising: modifications to the probability table based on quantitative observations of the natural system associated with the root node. 43. The Bayesian network of claim 38 where the one or more leaf nodes comprises: a probability table for each leaf node; each probability table having a respective one or more predefined states; and each predefined state having associated with it a probability that the leaf node is in that state. 44. The Bayesian network of claim 38 where each leaf node has a predefined number of inputs and where creating a probability table for each leaf node comprises: creating a probability table having the respective predefined number of input dimensions. 45. The Bayesian network of claim 44 where creating the probability table for each leaf node comprises: data reflecting quantitative observations of a natural system associated with the leaf node. 46. The Bayesian network of claim 45 further comprising: modifications the probability table based on quantitative observations of the natural system associated with the leaf node. 47. The Bayesian network of claim 38 where the one or more intermediate nodes comprises: a probability table for each intermediate node; each probability table having a respective one or more predefined states; and each predefined state having associated with it a probability that the intermediate node is in that state. 48. The Bayesian network of claim 38 where each intermediate node has a predefined number of inputs and where the probability table for each intermediate node comprises: a respective predefined number of input dimensions. 49. The Bayesian network of claim 48 where the probability table for each intermediate node comprises: data reflecting quantitative observations of a natural system associated with the intermediate node. 50. The Bayesian network of claim 49 further comprising: modifications to the probability table based on quantitative observations of the natural system associated with the intermediate node. 51. A method for predicting porosity and permeability comprising: predicting sand-grain composition and sand texture from tectonic setting, hinterland weathering and transport and basin transport and deposition using a Bayesian network; and predicting porosity and permeability from the predicted sand-grain composition and sand texture.

이 특허에 인용된 특허 (56)

이 특허를 인용한 특허 피인용횟수: 1

Skelt, Christopher Hugh. System and method for performing simultaneous petrophysical analysis of composition and texture of rock formations. USP20190110175383.

IPC 상위 출원인

내보내기

상세정보
내보내기 구분	파일저장 인쇄 메일전송
구성항목	관리번호, 국가코드, 자료구분, 상태, 출원번호, 출원일자, 공개번호, 공개일자, 등록번호, 등록일자, 발명명칭(한글), 발명명칭(영문), 출원인(한글), 출원인(영문), 출원인코드, 대표IPC 관리번호, 국가코드, 자료구분, 상태, 출원번호, 출원일자, 공개번호, 공개일자, 공고번호, 공고일자, 등록번호, 등록일자, 발명명칭(한글), 발명명칭(영문), 출원인(한글), 출원인(영문), 출원인코드, 대표출원인, 출원인국적, 출원인주소, 발명자, 발명자E, 발명자코드, 발명자주소, 발명자 우편번호, 발명자국적, 대표IPC, IPC코드, 요약, 미국특허분류, 대리인주소, 대리인코드, 대리인(한글), 대리인(영문), 국제공개일자, 국제공개번호, 국제출원일자, 국제출원번호, 우선권, 우선권주장일, 우선권국가, 우선권출원번호, 원출원일자, 원출원번호, 지정국, Citing Patents, Cited Patents
저장형식	Text(ASCII format) Excel format PIAS분석(.xls)
메일정보	받는사람 (필수) @ 보내는사람 (선택) @ 제목 내용 KISTI 검색결과 이메일 서비스
안내	> 총 건의 자료가 검색되었습니다. > 다운받으실 자료의 인덱스를 입력하세요. (1-10,000) > 검색결과의 순서대로 최대 10,000건 까지 다운로드가 가능합니다. > 데이타가 많을 경우 속도가 느려질 수 있습니다.(최대 2~3분 소요) > 다운로드 파일은 UTF-8 형태로 저장됩니다. 파일의 내용이 제대로 보이지 않을실 때는 웹브라우저 상단의 보기 -> 인코딩 -> 자동선택 여부를 확인하십시오 > ~ > Text(ASCII format) Excel format

과학기술 지식인프라 ScienceON

ScienceON Chatbot

연합인증

특허 상세정보

Predicting sand-grain composition and sand texture

초록
▼

대표
청구항
▼

이 특허에 인용된 특허 (56)

이 특허를 인용한 특허 피인용횟수: 1

IPC 상위 출원인

과제정보

상세정보조회

원문조회

IPC 상세정보(version : 201201)

연구과제 성과물(0)

참여연구원의 다른 문헌(0)

처리중입니다.

잠시만 기다려주세요.

ScienceON Chatbot

연합인증

특허 상세정보

Predicting sand-grain composition and sand texture

초록 ▼

대표청구항 ▼

이 특허에 인용된 특허 (56)

이 특허를 인용한 특허 피인용횟수: 1

IPC 상위 출원인

과제정보

상세정보조회

원문조회

IPC 상세정보(version : 201201)

연구과제 성과물(0)

참여연구원의 다른 문헌(0)

처리중입니다.

잠시만 기다려주세요.

초록
▼

대표
청구항
▼