Managing Risks Using Integrated Production Models: Process Description
Authors: Chow C.V. , Arnondin M.C. 
Published: June 2014

Technology Today Series articles are general, descriptive representations that summarize the state of the art in an area of technology by describing recent developments for readers who are not specialists in the topics discussed. Written by individuals recognized as experts in the area, these articles provide key references to more definitive work and present specific details only to illustrate the technology. Purpose: to inform the general readership of recent advances in various areas of petroleum engineering.

Summary
This paper describes an asset-management-analysis process that helps to quantify and manage uncertainty associated with field-development design, implementation, and operation. The risk-based integrated production model (RIPM) process is based on the principles of integration, quantification, and validation to define uncertainty in total-system performance and the associated economic impact of each delineated scenario. This process provides a more robust understanding of the risks and potential value of the prospective enterprise on an ongoing basis.

Introduction
Complex producing systems are often divided into subsystems (reservoir, well, and surface facilities) that typically are treated independently in both design and operations. Historically, each engineering function models and optimizes its component of the system on the basis of "local" instead of "global," or system, criteria and hands off the results for successive downstream functional analysis. The results from deterministic technical analyses that address specific problems in the different subsystems are then weakly linked (if linked at all) manually by the user (Fig. 1). Sometimes, because of timing constraints or lack of information, a component of the system is skipped or addressed superficially. This "linear" process is cumbersome and slow, limiting both evaluation of alternative options during the design phase of a project and the ability to react to new information during the implementation and operation phases. The system's design and subsequent performance suffer because optimization is managed at the subsystem level only and one subsystem's design may constrain the overall system unnecessarily.
Recent advances in integration have enhanced production optimization and asset-management-analysis processes. Analysis with "dynamically" linked tools (i.e., without user interface) increased field productivity by an estimated 15% over base decline in a mature optimized field.1 Integration in reservoir-management analysis also increased reserves recovered, productivity, and cost savings.2 Previous approaches to managing day-to-day operations and production-optimization activities, however, relied on a deterministic approach, which tends to overlook uncertainty in data, model, and/or assumptions. An improved process also is needed to link production-optimization and reservoir-management activities to achieve real-time reservoir management (i.e., to process data generated in a subsystem, analyze its impact on the global system, and enact appropriate changes to the depletion plan, all in a short time).
The RIPM analysis process was developed to address the disadvantages associated with the linear approach. RIPM encompasses both the process and the tools that facilitate making optimal and strategic decisions in design, implementation, and operation phases of a project to enhance the value of an asset over its life. Three key principles of integration, quantification, and validation are applied throughout the process. Integration evaluates the impact of an option on the entire system; quantification compares different options on a consistent, absolute basis; and validation identifies gaps in data and deficiencies in technical models to define uncertainty and subsequent avenues for improvement. The tools used to develop the quantitative models vary from application to application, depending on problem complexity, solution detail, and time required for solution. Tools are selected on the basis of their ability to provide quantitative solutions while adhering to the process principles.
The approach uses dynamically linked models of the reservoir, well, and surface-facility subsystems [i.e., integrated production model (IPM)] to evaluate system performance quickly (Fig. 2). Risk caused by uncertainty in assumptions, data (e.g., physical parameters, economic data), or model predictions is also quantified when appropriate and integrated with the IPM to give an RIPM. For example, input variables may have a range of possible values rather than a single point, resulting in a probable distribution of outcomes. The result is an iterative process to evaluate alternative options for design quickly and to react to new data during implementation or operation phases of a project over the life of an asset. The following sections describe the process and the available analytical tools. Ref. 3 will describe example applications.

Process Description
The process consists of six fundamental building blocks:identify alternative options/scenarios,collect data,construct models,analyze performance on subsystem and system levels,analyze options with associated uncertainty, anddecide on a strategic path.
Because of the iterative nature of the process, the sequencing of the building blocks can differ, depending on the problem. In one application, the process might start with the identification of options, in another, with analysis of data uncertainty. In either case, the process is customized to the problem so that the solution is not restricted.