Page 288 - Intelligent Digital Oil And Gas Fields

236 Intelligent Digital Oil and Gas Fields

6.4.2 Challenges and Ways Forward
In the last decade, IAM frameworks have evolved from being a promising
approach for the systematic management of oil and gas assets to sophisticated
and complex workflows that have delivered value in assets worldwide, facil-
itating high-level optimization and decision support in real-time operational
domains. However, IAM process and workflows may involve considerable
engineer time, human resources, and computer services, and thus it may take
several weeks to find a global optimum solution. Bottom-line, there are sev-
eral types of optimization techniques but these may not be completely acces-
sible or unified and thus require a lot of knowledge before implementing.
With active expansion and implementation of DOF operations, IAM prac-
tices are now transforming into computationally and operationally intensive
environments that involve “continuous series of decisions based on multiple
criteria including safety, environmental policy, component reliability, effi-
cient capital and operating expenditures and revenue” (Zhang et al., 2006).
However, challenges still exist that must be addressed before IAM
workflows become mainstream components of DOF operations. Here’s
what we see as the top five challenges:
1. The engineering and management organizations need to efficiently
adapt the advanced modeling and analysis IAMod environments where
the relevant groups and teams will have access to all the data and models
at all times.
2. The IAM framework requires a substantial effort and multidisciplinary
expertise to develop high quality and accurate models for the field life
cycle. Organizations must validate the value of IAM investment and
resource utilization toward field performance. Most of the oil and gas
operating companies are still overly compartmentalized in their opera-
tional and business models to facilitate an efficient execution of IAM
workflows. As emphasized in Cosentino (2001), the process of integrat-
ing different disciplines to perform an integrated reservoir study, requires
a “change of focus.” An example of a successful IAM practice that lever-
ages and deploys the studies decision synergy (SDS) can be found in
Elrafie et al. (2010). This practice represents an efficient unification plat-
form that improves the quality of reservoir modeling through the cross-
discipline integration of geology, characterization, engineering, history
matching, and prediction. The benefits from its execution are gained
through improved reserve quantification and optimized field develop-
ment planning decisions.
3. The data required to build integrated asset models are often stored in dis-
parate locations, are in structured and unstructured formats, are not