A new petrophysical modeling workflow for fractured granite basement reservoir in Cuu long basin, offshore Vietnam

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

1 被引用数 (Scopus)

抄録

Granite fractured basement reservoir contributes higher 40% of the world's oil and gas reserves. However, geological modeling of fractured reservoirs is complex and presents unique challenges in comparison with conventional reservoirs. It is extremely difficult to achieve the best results for a future development plan. This research presented the new workflow to enhance the accuracy of porosity and permeability models for a fractured reservoir in offshore Vietnam by using Artificial Neural Network (ANN) and co-kriging method. ANN was employed to solve problems that conventional modeling has not been successful. The seismic attributes selection was used for initial ANN generation. Then, the prediction property model was established through ANN training process. Well log data was used for correlation to cross-validation the predictive models. Next, the co-kriging algorithm was created the porosity and permeability models. Also, the Drill Stem Test (DST) data was used for history matching models to confirm the Co-kriging approach. The history matching was iterated until the geological model achieved the best matching with DST data. The history match shown the excellent fitting between simulation model and measurement data. Overall, we conclude that ANN and co-kriging are useful method for developing reliable workflow in fracture basement reservoir.

本文言語英語
ホスト出版物のタイトル81st EAGE Conference and Exhibition 2019
出版社EAGE Publishing BV
ISBN(電子版)9789462822894
出版ステータス出版済み - 6 3 2019
イベント81st EAGE Conference and Exhibition 2019 - London, 英国
継続期間: 6 3 20196 6 2019

出版物シリーズ

名前81st EAGE Conference and Exhibition 2019

会議

会議81st EAGE Conference and Exhibition 2019
Country英国
CityLondon
Period6/3/196/6/19

All Science Journal Classification (ASJC) codes

  • Geochemistry and Petrology
  • Geophysics

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