Integrated Decision Based Planning with Data Centric Approach: A Novel Way for Successful Delivering of a Lean FDP for a Complex Carbonate Reservoir, North Oman
Authors: Behera Chaitanya , Ghufaili Ali , Wassing Bart , Ghulam Junaid , Hadidi Shahab , Hamdoun Lana , Obilaja Jane-Frances , Qayum Shahid , Asma Dhuhli , Nabhani Kahlan , Khabouri Khalid , Annia Pino Carlos Eduardo , Khadhuri Shaikhan , Wahaibi Hamed 
Published: November 2018

This paper expounds the value of integrated decision based planning in delivering field development plan (FDP) in a LEAN way. The basic philosophy of lean is waste minimization or elimination of non-value adding activities to improve efficiency, quality and lead-time. Integrated decision based planning is considered as the most pragmatic and efficient approach in integrated reservoir modelling process. Dynamic modeling is the most preferred tool assisting subsurface decisions making. Nevertheless, the data centric approach with support of scaled reservoir model has the advantage over the conventional full field physics-based models specially, in case of a complex reservoir. Embracing the basic lean principles with focused decision based reservoir modeling strategy can establish a new level of performance within the organization in delivering FDPs.

The Saih Rawl oil field (SRS) in North Oman is a thin Lower Cretaceous carbonate reservoir with post diagenetic imprint. Post oil fill structural change has resulted in re-saturation and oil trapping due to local capillary imbibitions. The complexities resulting from the tilted water contact, hysteresis in oil mobility and Sor variation with depth, pose a huge challenge in dynamic simulation. In addition, drilling feasibility for horizontal infill wells was quite challenging due to subsurface collision issues and rig footprint interference with existing surface facilities. Integrated decision based planning, linked to the subsurface and surface decisions was adopted for framing the integrated reservoir modeling (IRM) strategy. The IRM strategy with Decision Based Models (DBM), including analytical and sector simulation models, were used to understand the sweep pattern, locate the remaining oil and rank the various water-flood patterns. Data analysis including normalized decline-curve-analysis (DCA) based conduit models and comprehensive field performance analysis using Spotfire (an integrated data visualization and analysis tool by TIBCO), was used to understand the key reservoir management risks and infill potential. Throughout the process, the basic philosophy of lean was adopted embracing several lean tools to improve productivity, quality and lead-time.

Out of 12 subsurface feasible options studied, the proposed optimum option envisages an increase in the oil recovery factor by 9% by drilling an additional 92 infill wells in 22 patterns. The successful completion of frontend loading phase of SRS project has achieved reducing in the FDP study time to 19 months compared to an average of 36 months in the past and project implementation 4 years ahead of the original plan. Fast tracking of the project implementation was possible due to standardization of the equipment, maximum utilization of the existing infrastructure and constructive collaboration with the stakeholders. The key enablers for the successful delivering of the SRS FDP study were mainly the integrated decision based planning with data centric approach in reservoir modeling workflow and adoption of basic lean principles

This approach emphasizes the importance of adopting lean tools in frontend delivery process. The decision based planning with reservoir models linked to the project decision can significantly improve the efficiency and quality of the FDP. The stakeholder alignment and strong collaboration with key stakeholders of the project can further reduce the lead-time of project execution. The decision based IRM planning used for this study sets a benchmark for future FDP studies. The Urban Plan study approach for this project has also become the standard for other LEAN FDPs.