How reservoir uncertainty and sensitivity studies can lead to better field development and hardware decisions

Garrett M. Leahy and Lifang Wang propose a new approach to reducing uncertainties in key production metrics that brings risk management back to the centre of the decision-making framework. Running producing oilfields is a complex business. Production managers need to balance the evolution of production from dynamic and geologic changes with ageing fields and infrastructure and the failure of facilities and hardware. However, optimizing production is in itself a complex task as there are many variables and levers that reservoir engineers need to access. Injection rates, fluids, and production chokes can all influence day-to-day operations at the wellhead and ultimately the long-term productivity (and value) of the reservoir. Not all of these levers are created equally: the day-to-day management of well chokes to optimize flow, for example, can be significantly less risky (and costly) than a new injection or enhanced oil recovery programme. Rather than simply jumping on the big-data, ‘pervasive sensing’ bandwagon to solve this problem, we propose a more strategic approach that brings risk management back to the centre of the decision-making framework. Conventional wisdom has been that making measurements of key flow parameters (water cut, flow rate, temperature, downhole pressure, etc) is the key to accurate production forecasting. For example, how much water and sand is being produced and from which wells? What impact does this have on processing facilities and hardware choices? How can threats to production caused by water breakthrough, corrosion, erosion and hydrates, be pre-empted?