Page 274 - Intelligent Digital Oil And Gas Fields

224 Intelligent Digital Oil and Gas Fields

Noise Input System Output Noise
(reservoir, wells
& facilities)
Controllable
input Optimization

Control Sensors
algorithms

System model

Parameter
updating

Fig. 6.7 Schematic representation of closed-loop reservoir management (CLoReM)
workflow. (With permission from Jansen, J.D., Douma, S.D., Brouwer, D.R., van den Hof,
P.M.J., Bosgra, O.H., Heemink, A.W., 2009. Closed-Loop Reservoir Management.
SPE-119098-MS. https://doi.org/10.2118/119098-MS.)

assisted history matching (AHM) techniques under uncertainty. The objec-
tive of so-called “big-loop workflows” (Wiluweit et al., 2015; Kumar et al.,
2017) is to generate reservoir simulation models that adequately quantify
multiparameter static and dynamic uncertainties with associated risks and
to render better predictive value for the field and asset development plan-
ning. Fig. 6.8 shows a schematic representation of a big-loop workflow.

6.4 OPTIMIZATION OF MODERN DOF ASSETS

This section outlines examples and applications of optimizing DOF
assets using IAM workflows that integrate subsurface reservoir models
and surface production network models. With rapidly expanding asset com-
plexity driven by the size of the models and vast amounts of acquired and
processed data, the major challenge of IAM still represents the coupling
of the dynamic reservoir simulation model and the surface facilities into a
single, integrated platform that allows the simultaneous simulation of the
entire oil and gas system, “all in-one” and forecasts the asset’s performance
for the purpose of management.
The modern DOF system requires state-of-the art systems integration
that can, for example, diagnose and address the operational challenges such