Maximizing recovery and production used to be the main goal of field development plans, but that may be in the past. Extended periods of low oil prices and the incumbent use of data-driven work flows have triggered changes in the development of field development plans toward maximizing economic margins.
Recent advances in field development are not based on specific technical breakthroughs (although shale oil and gas and deep oil owe a lot to those) but on economic models and scenarios run at the very beginning, before any dollar is invested. This is true for companies large or small, public or private, and for national oil companies.
Major SPE conferences in 2017 and 2018 were hubs for numerous technical papers that portray the new trends in field development, such as basing decisions on the analysis of economic models and profitability elements. Certainly, operating companies are migrating from complex, lengthy, and bureaucratic approaches to capital-expenditure (CAPEX) decisions and approvals for field development plans to more data-driven decisions based on economic analysis using new tools.
Artificial intelligence, supervised/guided machine learning, and massive and advanced data analytics play fundamental roles in the preparation of new field development plans, where well, field, and facilities information is shaped into high-resolution models that consider the micro and the macro elements. What previously was considered luxurious, such as high-resolution 3D seismic data and digital technologies, has become fundamental. Focusing on the maximization of margins using data-driven work flows enables better decisions at the field development stage and has yielded dramatic CAPEX/operational-expenditure optimizations and reductions, shrinking the time to first oil and triggering approvals of plans quicker.
I am confident you will enjoy reading these articles outlining the new trends in field development. The industry is evolving toward a data-driven approach to profitability, and we need to catch the wave.
This Month's Technical Papers
Small-Field Approach Holds Promise for Operators in Southeast Asia
Machine Learning Overcomes Challenges of Selecting Locations for Infill Wells
Data-Analytics Method Helps Engineers Optimize Well Placement Under Uncertainty
Recommended Additional Reading
OTC 28791 Optimized Field Development Through Integrating Field Network With Dynamic Reservoir Model by Mahanaz Hatvik, TechnipFMC, et al.
SPE 190239 Machine-Learning-Based Optimization of Well Locations and WAG Parameters Under Geologic Uncertainty by Azor Nwachukwu, The University of Texas at Austin, et al.
OTC 28970 A Universal Field Development Approach for Advancing Stalled GOM Deepwater Projects by Richard D’Souza, Granherne, et al.
| Maria A. Capello, SPE, is an executive adviser with the Kuwait Oil Company (KOC) for the North Kuwait Asset, advancing strategic initiatives in reservoir-management best practices for all assets of KOC, providing training in diversity and other areas for upstream and downstream companies. She is an experienced industry consultant and an expert in field development and monitoring strategies. Capello has worked in Latin America, the US, and the Middle East. She holds an MS degree in geophysics from the Colorado School of Mines. She is author of Learned in the Trenches: Insights on Leadership and Resilience Compiled by Two Women Leaders in Energy with Hosnia Hashim, published in 2018. Capello is an SPE Distinguished Lecturer for 2018, an Honorary Lecturer of the Society of Exploration Geophysicists (SEG) for 2018/2019, and is Director at-Large of SEG. She received the GRIT Award from PInkPetro in 2018, is the recipient of a SEG Special Commendation Award, and has received SPE Distinguished Membership and international service awards. She serves on the JPT Editorial Committee and can be reached at mcapello@kockw.com. . |
