We are pleased to announce that the forthcoming 121st JFES Chapter Meeting will be held on July 25th with the detail as follows. We encourage you all to peruse the below program and register your attendance from the link by July 18th.
This event is designed as online.
Date & Time: July 25th, Tuesday, 15:30 – 17:30 (JST)
Online Participation: The access link will be informed to registrants.
Contact： info (at mark) spwla-jfes.org
Program:
Chair: Kentaro Hasebe (INPEX)
Presentation 1:
Title: Green Tuff reservoir rock type classification by integrating petrography and petrophysics data (Case study in Higashi Kashiwazaki gas field
Keitaro Kojima（INPEX）
Language：Japanese
Higashi-Kashiwazaki gas field (HK field) located Niigata prefecture in Japan, has long history of gas production from Miocene volcanic reservoir so-called “Green tuff”. Currently, HK field attracts attention for usage of Carbon Capture Utilization and Storage (CCUS). We revisited rock type classification and built geological model as follows.
First, we defined rock type classification based on microscopy observation and petrological analysis (XRD/XRF) of cutting samples. The reservoir of HK filed is divided to 4 rock types: Rhyolitic Lava Facies (Lv), Rhyolitic Breccia/Auto-Brecciated Lava Facies (Br), Resedimented Hyaloclastite/Tuff Facies (Tf) and Mafic Lava/Tuff facies (Maf). Second, in order to maximize data points for geological model, we established rock type prediction scheme practically with limited wireline logs (Gamma Ray, Resistivity, Density and Neutron). Then, considering volcanic rock dimension from outcrop analogy, we interpreted inter-well correlation and build facies model.
The new rock type by integrating petrological analysis and petrophysical prediction simplifies complex volcanic reservoir and enables us to build static model quantitatively and efficiently.
Presentation 2:
Title: DX in Subsurface Evaluation: INPEX’s Initiatives and Challenges
Hiroyuki Inoue（INPEX）
Language：Japanese
Since the late 2010s, INPEX has been working on the application of machine learning for subsurface evaluation and the centralization of subsurface data onto a cloud platform as part of its digital transformation (DX) initiatives. Although these efforts have yielded the improvements in evaluation tasks, we continue to face challenges as we strive for our goal of DX, which is to strengthen the upstream business and to create new value. In this presentation, we will introduce the achievements and issues in these endeavors, focusing on case studies of machine learning application, construction of a cloud data platform, and the use of high-performance cloud computing.
