Page 247 - Intelligent Digital Oil And Gas Fields

CHAPTER SIX

Integrated Asset Management

and Optimization Workflows

Contents
6.1 Introduction to IAM and Optimization 198
6.2 Optimization Approaches 199
6.2.1 Single- vs. Multiobjective Optimization 202
6.2.2 Local vs. Global Optimization 206
6.2.3 Optimization Under Uncertainty 209
6.3 Advanced Model Calibration With Assisted History Matching 214
6.3.1 Model Parameterization and Dimensionality Reduction 217
6.3.2 Bayesian Inference and Updating 219
6.3.3 Data Assimilation 221
6.3.4 Closed-Loop Model Updating 223
6.4 Optimization of Modern DOF Assets 224
6.4.1 Applications of IAM and Associated Work Processes 231
6.4.2 Challenges and Ways Forward 236
References 240

Digital oil field (DOF) systems conventionally have focused on wells, pro-
duction, and operations. However, DOF is expanding its footprint into field
decisions and management. Thus, to optimize production and recovery,
production system models are increasingly being integrated with reservoir
models. DOF systems now deploy three-dimensional (3D)-coupled subsur-
face and surface models that, when calibrated (i.e., history matched), provide
short- and long-term forecasts of asset production and performance. The
main objective of this chapter is to give an overview of the modern inte-
grated asset modeling (IAMod) practices and outline techniques and
workflows for optimization and decision-driven forecasts of DOF systems
that is integrated asset management (IAM), including model and data uncer-
tainty. In Section 1.5 of Chapter 1, we introduced the concept of optimi-
zation process in DOF. The optimization process is related to the area of
real-time production optimization for artificial lift (GL, ESP, PCP, etc.)
and also applied to maximize the company indicators such as NPV, IRR,

Intelligent Digital Oil and Gas Fields © 2018 Elsevier Inc. 197
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