Page 250 - Intelligent Digital Oil And Gas Fields

200 Intelligent Digital Oil and Gas Fields

and optimization methods have emerged and have been deployed in assets
worldwide. For example, the implementation of top-down reservoir models
(TDRM) (Williams et al., 2004) has been proven to add 20% to the net pre-
sent value (NPV) and has proven significantly more efficient than the manual
history-matching process, when applied to the optimization of the carbonate
reservoir in the North Sea. While manual history matching gave very poor
results due to >80 variables/parameters, the TDRM approach produced an
acceptable match in less than a month and showed 50 million to 150 million
more barrels of oil than the manual approach (Cope, 2011). Moreover, the
use of a multipurpose environment for parallel optimization (MEPO)
(Schulze-Riegert et al., 2001) has helped operators analyze—in half a
day—hundreds of operating scenarios and proposed the NPV range solu-
tions with the worst case of $13 million and the best case of $27 million
(Cope, 2011). In addition, numerous oil and gas optimization studies have
recently been published in the literature and deployed for these purposes:
• Dynamic optimization of waterflooding with smart wells using optimal
control theory, a gradient-based optimization technique (Brouwer and
Jansen, 2002; Brouwer et al., 2004) and adjoint-based optimal control
(Sarma et al., 2006, 2008; Suwartadi et al., 2011).
• Waterflood performance management and optimization using data-
driven predictive analytics from capacitance resistance models (CRM)
for rapid characterization of inter-well connectivity and continuous
update of injection rates to maximize oil production and recovery
(Kansao et al., 2017).
• Multiobjective optimization with applications to model calibration and
uncertainty quantification (Schulze-Riegert et al., 2007).
• Closed-loop production optimization and management using robust,
constrained optimization of short- and long-term NPV (Wang et al.,
2007; Chen et al., 2012) and ensemble-based optimization, using data
assimilation techniques (Chen et al., 2009).
• Real-time optimization and proactive control of waterflood perfor-
mance in intelligent wells, equipped by wellbore pressure and temper-
ature sensors and inflow control valves (ICVs) (Temizel et al., 2015) and
real-time reservoir management using multiscale adaptive optimization
and control (Saputelli et al., 2006).
• Top-down intelligent reservoir modeling (TDIRM) (Gomez et al., 2009),
which integrates traditional reservoir engineering analysis with artificial
intelligence and data mining [artificial neural networks (ANNs), fuzzy sets]
to predict reservoir performance and optimize development strategies.