| Autor: |
Taihei Omura, Saja Almurshidi, Fatema Aljaberi, Russell Dedmon, Naeema Khouri, Budoor Al-Shehhi, Osama Al Zinati, Ravi Shekhar, Khaled Attalah, Gary Ottinger, Abdulla Al Neyadi, Nada Abousayed, Hesham Abdulla Al Zawa, Corey Wendland, Omar Al Dhaleei, Chukwudi Obeta, Mohammad Yunus Khan |
| Rok vydání: |
2017 |
| Předmět: |
|
| Zdroj: |
Day 4 Thu, November 16, 2017. |
| DOI: |
10.2118/188745-ms |
| Popis: |
As part of the ongoing development of a large offshore oil field, an asset owner places a strong emphasis on continuous improvement of the established framework for integrated post-drill well analysis. The geology of the candidate field is complex and the occurrence and distribution of the extreme permeability features that dictate early water production is highly uncertain. While much effort is devoted to mitigating their adverse impact through proper integration of surveillance data for accurate well planning, post-drill outcomes can still diverge significantly from pre-drill expectations. Several wells have been drilled in the production build-up campaign, including ground-breaking pilots and many more are following in very quick succession as part of the life cycle strategy for the field. Due to high drilling frequency, the challenges of assimilating learnings through conventional post-drill analysis for optimization of future drill wells can be enormous. To apply key lessons from these wells in building quick baseline knowledge for reservoir model update and drill plan optimization, the modeling and development team have developed an improved workflow for integrated post-drill analysis. The workflow leverages the full benefit of collaboration between multi-disciplinary teams to integrate 3D seismic data, multiple well information (including geologic reports, well logs and petrophysical results) and surveillance data from new drill wells to benchmark pre-drill expectations. An important aspect of the approach is the quick incorporation of drilling results into static and dynamic models via a cycled, closed-loop workflow for quick assessment of model fidelity through an evergreen update process. A multifunctional post-drill analysis facilitates critical consideration of well results to capture significant learnings that influence future drill well and data acquisition optimization, reservoir model history match and prediction enhancements, and identification of drilling hazards and geological features that affect reservoir performance. This paper describes the methodology used to plan and implement post-drill well analysis within a fast paced and high drill frequency environment. Key elements of the methodology are described through the use of a case study example, and include: Standardized subsurface workflow, comparison of post-drill well results with pre-drill well expectations, identification and documentation of significant observations and lessons learned improvement of history match & predictive capability of reservoir models and integration with other drill-well delivery processes. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
|
