公开(公告)号：US20160273314A1

公开(公告)日：2016-09-22

申请号：US14662320

申请日：2015-03-19

Applicant: Hitachi, Ltd.

Inventor： Iwao TANUMA ,  Shouchun PENG ,  Hiroshi YOSHIKAWA ,  Masami YAMASAKI ,  Umeshwar DAYAL ,  Ravigopal VENNELAKANTI ,  Anshuman SAHU

IPC: E21B41/00 ,  G06N99/00 ,  G06N7/00 ,  G06Q50/02 ,  G06Q10/06

CPC classification number: G06N99/005 ,  E21B43/00 ,  G06N7/005 ,  G06Q10/067 ,  G06Q50/02

Abstract: Provided is a production amount prediction system including: a storage unit which stores a production amount prediction model which is based on resources information including a resources amount obtained in a previously drilled wellbore and a resources recovery probability in the vicinity thereof; an input unit which receives a trajectory coordinate of a planned wellbore as an input; a production amount prediction unit which calculates a production amount of the planned wellbore based on the production amount prediction model by using a degree of influence of the previous wellbore on the planned wellbore as at least one parameter; and a display unit which displays the production amount of resources of the planned wellbore calculated by the production amount prediction unit.

Abstract translation: 提供一种生产量预测系统，包括：存储单元，其存储基于包括在先前钻井中获得的资源量的资源信息和其附近的资源恢复概率的生产量预测模型; 接收计划井筒的轨迹坐标作为输入的输入单元; 生产量预测单元，其基于所述生产量预测模型，通过使用所述先前井眼对所述计划井眼的影响程度作为至少一个参数来计算所述计划井筒的生产量; 以及显示单元，显示由生产量预测单元计算出的计划井筒的资源的生成量。

公开(公告)号：US20180075235A1

公开(公告)日：2018-03-15

申请号：US15495213

申请日：2017-04-24

Applicant: Hitachi, Ltd.

Inventor： Yoshiyuki TAJIMA ,  Susumu SERITA ,  Masami YAMASAKI

IPC: G06F21/55

CPC classification number: G06F21/554 ,  G06F2221/034

Abstract: An abnormality detection system is configured to (a) convert, based on a prescribed rule, a time-sequential event included in a log output by a monitoring target system into a symbolized event; (b) learn, based on a normal-time log symbolized in (a), a symbolized event sequence, which appears in a same pattern, as a frequently-appearing pattern; and (c) detect an occurrence or a nonoccurrence of an abnormality, based on whether not the frequently-appearing pattern is occurring in a monitoring-time log symbolized in (a).