Improving Operations Using Model-Based Decision Support for E&P Assets

Abstract

Management Online applications using models of process and utilities plants have delivered benefits to refinery, chemicals, and utilities processes for more than two decades. The scope and objectives of these applications range from monitoring individual equipment performance to closed-loop optimization of operating profit across complex, integrated plantwide assets. E&P operators have historically focused on adding reserves through exploration. This is what drives a company's share price. As a result, E&P operators have not widely embraced the technology available to optimize their existing assets. With exploration opportunities becoming more difficult and costly, optimization of existing assets is a "new" frontier for adding shareholder value. Early adopters have deployed online models in the E&P business with significant benefit. Technology improvements and experience have now opened up opportunities that allow more widespread and accelerated deployment of real-time, model-based decision support applications for E&P assets. This article outlines where online models can have significant impact on improving several aspects of E&P operations, the technology involved, as well as the approach and best practices necessary for successful deployment and maintenance of these applications. Most process simulators include configurable models of process equipment including two- and three-phase flash drums (gas/oil separators); pumps; compressors (centrifugal, reciprocating, axial, screw, etc.); drivers such as motors, steam turbines, and gas turbines; heat exchangers; distillation columns; absorbers; strippers; valves; mixers; splitters; piping; and other unit operations. These unit operations can be configured together to model small parts of an E&P asset; a single platform and its processing equipment, or multiple assets interconnected with complex piping networks. It is challenging but now practical to deploy scalable model-based applications which monitor, simulate, and optimize E&P assets in real time in operating environments adding benefits that are appreciable and measurable. Below the surface E&P assets are addressed by reservoir modeling that shares little in common with the aforementioned process-equipment models. These reservoir models are not practical to use in real time because of lack of real-time data collection, slow reaction to production changes, and their very large computation times. However, practical empirical models of wells are successfully employed along with the above-surface models so that the first-order effects of changing above-surface conditions such as pressures on flows and compositions are taken into account.