Abstract
Significant reserves and production growth are often achieved by bringing green fields onto production. Green field development is an intricate process and the quality of activities and decisions taken in the early stages significantly impact project value. Poor definition of the field resource base and the lack of practical, cost effective development options are a sure road towards value erosion. Thus a critical aspect of the development process is the evaluation/validation of the project resource/reserves base from which the key project economic parameters are determined. Resource evaluation for New Field Developments has changed dramatically over the past decade as no longer is "the answer" obtained by building and testing one or two deterministic models.
Modern Major Capital Projects are expected to develop fully probabilistic production forecasts and ranges of Original Oil in Place (OOIP) and Reserves in order to assess the entire project value profile. Typically, this involves creating multiple static earth models representing different geologic realizations coupled with a range of possible dynamic reservoir and operating conditions to yield numerical simulation models. Attempting to generate probabilistic forecasts one combination at a time or even with the application of structured uncertainty assessment techniques such as Design of Experiment (DoE) could easily push the project's evaluation time to months, sometimes even years. The dilemma thus becomes how to make a complete project go/no-go decision with the right amount of technical rigor while avoiding long resource evaluation cycle time on projects that potentially may not move into execution. In this increasingly resource constrained environment, both in terms of personnel and funding, there is need for a faster, better suited way to understand the key value drivers and the range of project value early in a field development. The approach presented here preserves technical rigor while leading to quicker, more efficient project (go/no go) decisions.
The Madu-Anyala Development Project investigated a cluster of 14 reservoirs in a joined development. Application of a streamlined method of earth modelling, in combination with integrated dynamic modelling of subsurface, wellbore, and surface conditions in a systematic manner described in the paper, provided probabilistic Original Oil in Place, recoverable reserves, and three-phase production profiles with a cycle time of only 4 months. The profiles, along with the concurrently generated facilities and drilling cost estimates, allowed for a fast, but reliable, assessment of project value early in the development.
