Integration of reservoir planning and surveillance : history to prediction
Authors: Walker Gregory James , Bishop Simon Richard , Williams Glyn , Reddick Chris 
Published: February 2016

Abstract
The concept of depletion planning covers a variety of time scales and levels of investment, and has to cope with a level of risk in outcome that the operations team seeks to reduce through surveillance. One approach to bring surveillance and planning together in a quantitative manner is to use a series of discrete reservoir descriptions.
There have been some recent advances in quantifying the value of surveillance1,2 and in developing depletion options3,4,5 that span the range from pattern balance to new field development. Previous work has generally been based on a single reservoir description, with the assumption that a pattern balance option developed on the single model is suitable for application in the field. This paper covers the extension to multiple models, where we can seek the optimum choice of things that we can control in the depletion plan across the multiple reservoir models that we can't control. We can use surveillance to try to distinguish between models if we can identify the surveillance prize.
Informed by the surveillance prize, the next step is to test different surveillance options to search for the most effective, with a quantitative business value.
The surveillance most likely to be of use to pattern balancing is interference and multirate well testing, where the use of discrete models allows the reservoir engineer to explicitly test how the different reservoir models will behave.
All of this requires the efficient handling of large amounts of information, enabled by a combination of computer assisted tools such as BP's TDRM6 and effective reservoir monitoring and data mining through technologies such as the Field of the Future program7,8.

Background
There has been considerable work reported on the task of optimizing pattern balance9, which even for a single known reservoir can consume large amounts of simulation time.
A typical process has involved developing a depletion plan on a single model, and using adjoint techniques to find an improved production scheme10. The production and injection controls can include rate changes, but the bang-bang option where the chokes are either open or closed has the advantage of limiting the degrees of freedom whilst still allowing near optimal production11.
The step in this study is the use of multiple alternative history matches. If the history match process were performed correctly, we should have a range of models that captures the true reservoir description, as well as alternatives that show the range still allowed within the constraints of surveillance2,12 information. We can test the same depletion plan on the different alternatives and gain a better understanding of what outcomes are possible, and search for an operational approach that satisfy the requirements for managing risk and delivering incremental value. We have to be careful in the selection of models from the history match process, since any technique other than an exhaustive search must have introduced a bias. Selecting models at random from a list of those acceptable to surveillance is only acceptable if the search routine itself has not introduced a bias13.
The key result is that the smaller depletion plan options such as pattern balance are sensitive to the reservoir description. As a consequence, any production optimisation has to account for subsurface uncertainty. Of greatest concern is where the options are highly specific to each possible reservoir description. For Harding, the range in outcomes for a particular depletion plan precludes a clear decision as to which depletion plan is better, identifying a surveillance prize.