Developing, producing and delivering energy in a sustainable way
Japan Oil Development Co., Ltd. (JODCO), a subsidiary of INPEX CORPORATION (INPEX), Japan’s largest exploration and production company, has been involved in the development and production of offshore and onshore oil fields in Abu Dhabi for almost 50 years. Today, INPEX/JODCO is engaged in efforts to further enhance the production capacity of these oil fields, particularly at the Lower Zakum Oil Field as Asset Leader. Meanwhile, INPEX/JODCO works in close cooperation with ADNOC on the exploration of Onshore Block 4. The company is also involved in a variety of stakeholder engagement programs in the areas of education and cultural exchange.
Speaking at ADIPEC 2020 Virtual
President & CEO
Time: 16:45 – 17:30
Date: Tuesday, 10 November 2020
Transforming upstream oil and gas operations: preparing for a net zero future
Pressures to decarbonise energy have grown over the past few years, impacting companies’ social license to operate, their access to investors, and their stakeholder proposition. What strategies are companies putting in place to address demands for greater decarbonisation? What technologies and business models are enabling companies to take advantage of the opportunities created by the transition to low carbon energy?
General Manager of Technical Unit
JODCO Exploration Ltd
SPE-203261-MS: Paleo Environment Analysis Using Geochemical Data Of The Intrashelf Basin in Cenomanian, Cretaceous Of Abu Dhabi, Restore the sedimentary environment in UAE of Cenomanian, Late Cretaceous
To consider paleo environment reconstruction of Cenomanian, Cretaceous using chemostratigraphy, TOC measurement, geochemical and biomarker analysis.
Production Engineer, Production Engineering Group
Impact of WAX on Asphaltene Deposition under Jet Pump Operation as revealed by Asphaltene Inhibitor Field Test
This paper presents how an asphaltene problem in a jet pump operation well was solved. A new inhibitor was selected and tested for the well. Selected asphaltene inhibitor was efficient at dispersing asphaltenes. It achieved stable injection pressure and reduced asphaltene deposition amount. A follow-up laboratory test revealed the asphaltene deposition amount decreased by adding paraffin inhibitor. This field test result revealed the asphaltene and paraffin interaction in field scale.
Senior Research Engineer
INPEX Technical Research Center
Effect of Total Acid Number and Recovery Mode on Low-salinity EOR in Carbonates Presented by Takaaki Uetani, Hiromi Kaido, and Hideharu Yonebayashi (INPEX)
We present the main reason behind two low-salinity waterflooding (LSW) coreflood tests, that failed to demonstrate promising EOR response. Repeating the coreflood tests using the acid-enriched oil and injecting the low-salinity brine in the secondary and tertiary modes has led us that the total acid number (TAN) and the recovery mode appear to be the key successful factors for LSW in our carbonate system.
Geophysicist, Development Geology & Geophysics Group
Machine Learning Based Seismic Data Enhancement Towards Overcoming Geophysical Limitations
Acquisition of incomplete data, i.e., blended, sparsely-sampled and narrowband data, is an attractive strategy, provided that the recovery of complete data, i.e., deblended, well-sampled and broadband data, is attainable. We propose a machine-learning scheme that simultaneously performs deblending, trace reconstruction and low frequency extrapolation. Both synthetic and field data examples demonstrate the applicability of the proposed method. It is noteworthy that no discernible difference in prediction errors between extrapolated and preexisting frequencies is observed.
Data Analyst, Digital Development & Infrastructure Group
Facilitating The Identification Of The Nannofossil Species In Cretaceous Of Abu Dhabi Using Artificial Intelligence
Reliable chrono-stratigraphic correlation is crucial in the reservoir evaluation. Geological age determination based on nannofossils in Middle East gives a better control for the stratigraphic correlation, however it requires skilled experts and time for appraisal. In this study, we try to provide solution with emerging AI technology. The result shows that a deep-learning-based object detector reasonably identifies nannofossil species on micrographs and can deliver information useful for the geological age assessment.